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mmdetection
mmdetection-master/configs/faster_rcnn/metafile.yml
Collections: - Name: Faster R-CNN Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - FPN - RPN - ResNet - RoIPool Paper: URL: https://arxiv.org/abs/1506.01497 Title: "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" README: configs/faster_rcnn/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/faster_rcnn.py#L6 Version: v2.0.0 Models: - Name: faster_rcnn_r50_caffe_c4_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 35.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth - Name: faster_rcnn_r50_caffe_c4_mstrain_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 35.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth - Name: faster_rcnn_r50_caffe_dc5_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth - Name: faster_rcnn_r50_caffe_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py Metadata: Training Memory (GB): 3.8 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth - Name: faster_rcnn_r50_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py Metadata: Training Memory (GB): 4.0 inference time (ms/im): - value: 46.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth - Name: faster_rcnn_r50_fpn_fp16_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py Metadata: Training Memory (GB): 3.4 Training Techniques: - SGD with Momentum - Weight Decay - Mixed Precision Training inference time (ms/im): - value: 34.72 hardware: V100 backend: PyTorch batch size: 1 mode: FP16 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth - Name: faster_rcnn_r50_fpn_2x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py Metadata: Training Memory (GB): 4.0 inference time (ms/im): - value: 46.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth - Name: faster_rcnn_r101_caffe_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py Metadata: Training Memory (GB): 5.7 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth - Name: faster_rcnn_r101_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py Metadata: Training Memory (GB): 6.0 inference time (ms/im): - value: 64.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth - Name: faster_rcnn_r101_fpn_2x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py Metadata: Training Memory (GB): 6.0 inference time (ms/im): - value: 64.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth - Name: faster_rcnn_x101_32x4d_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 7.2 inference time (ms/im): - value: 72.46 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth - Name: faster_rcnn_x101_32x4d_fpn_2x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py Metadata: Training Memory (GB): 7.2 inference time (ms/im): - value: 72.46 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth - Name: faster_rcnn_x101_64x4d_fpn_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 10.3 inference time (ms/im): - value: 106.38 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth - Name: faster_rcnn_x101_64x4d_fpn_2x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py Metadata: Training Memory (GB): 10.3 inference time (ms/im): - value: 106.38 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth - Name: faster_rcnn_r50_fpn_iou_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.9 # re-release Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954-938e81f0.pth - Name: faster_rcnn_r50_fpn_giou_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth - Name: faster_rcnn_r50_fpn_bounded_iou_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth - Name: faster_rcnn_r50_caffe_dc5_mstrain_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth - Name: faster_rcnn_r50_caffe_dc5_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py Metadata: Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth - Name: faster_rcnn_r50_caffe_fpn_mstrain_2x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py Metadata: Training Memory (GB): 4.3 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth - Name: faster_rcnn_r50_caffe_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 3.7 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth - Name: faster_rcnn_r50_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 3.9 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth - Name: faster_rcnn_r101_caffe_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 5.6 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth - Name: faster_rcnn_r101_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 5.8 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth - Name: faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 7.0 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth - Name: faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 10.1 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth - Name: faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py Metadata: Training Memory (GB): 10.0 Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 43.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth - Name: faster_rcnn_r50_fpn_tnr-pretrain_1x_coco In Collection: Faster R-CNN Config: configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py Metadata: Training Memory (GB): 4.0 inference time (ms/im): - value: 46.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth
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mmdetection
mmdetection-master/configs/fcos/README.md
# FCOS > [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355) <!-- [ALGORITHM] --> ## Abstract We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143882011-45b234bc-d04b-4bbe-a822-94bec057ac86.png"/> </div> ## Results and Models | Backbone | Style | GN | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :------: | :---: | :-: | :------: | :----: | :-: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | caffe | Y | N | N | N | 1x | 3.6 | 22.7 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/20201227_180009.log.json) | | R-50 | caffe | Y | N | Y | N | 1x | 3.7 | - | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/20210105_135818.log.json) | | R-50 | caffe | Y | N | Y | Y | 1x | 3.8 | - | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/20210105_224556.log.json) | | R-101 | caffe | Y | N | N | N | 1x | 5.5 | 17.3 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/20210103_155046.log.json) | | Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :------: | :-----: | :-: | :------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | caffe | Y | Y | 2x | 2.6 | 22.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20201227_161900.log.json) | | R-101 | caffe | Y | Y | 2x | 5.5 | 17.3 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20210103_155046.log.json) | | X-101 | pytorch | Y | Y | 2x | 10.0 | 9.7 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/20210114_133041.log.json) | **Notes:** - The X-101 backbone is X-101-64x4d. - Tricks means setting `norm_on_bbox`, `centerness_on_reg`, `center_sampling` as `True`. - DCN means using `DCNv2` in both backbone and head. ## Citation ```latex @article{tian2019fcos, title={FCOS: Fully Convolutional One-Stage Object Detection}, author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, journal={arXiv preprint arXiv:1904.01355}, year={2019} } ```
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mmdetection-master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( backbone=dict( init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron2/resnet50_caffe')), bbox_head=dict( norm_on_bbox=True, centerness_on_reg=True, dcn_on_last_conv=False, center_sampling=True, conv_bias=True, loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), # training and testing settings test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) # dataset settings img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) optimizer_config = dict(_delete_=True, grad_clip=None) lr_config = dict(warmup='linear')
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mmdetection-master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( backbone=dict( dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True), init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron2/resnet50_caffe')), bbox_head=dict( norm_on_bbox=True, centerness_on_reg=True, dcn_on_last_conv=True, center_sampling=True, conv_bias=True, loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), # training and testing settings test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) # dataset settings img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) optimizer_config = dict(_delete_=True, grad_clip=None) lr_config = dict(warmup='linear')
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mmdetection
mmdetection-master/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
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mmdetection
mmdetection-master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron/resnet101_caffe')))
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mmdetection-master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron/resnet101_caffe'))) img_norm_cfg = dict( mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
_base_ = [ '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # model settings model = dict( type='FCOS', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, style='caffe', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron/resnet50_caffe')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_output', # use P5 num_outs=5, relu_before_extra_convs=True), bbox_head=dict( type='FCOSHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, strides=[8, 16, 32, 64, 128], loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='IoULoss', loss_weight=1.0), loss_centerness=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), # training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) img_norm_cfg = dict( mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # optimizer optimizer = dict( lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict( policy='step', warmup='constant', warmup_iters=500, warmup_ratio=1.0 / 3, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12)
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mmdetection
mmdetection-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
# TODO: Remove this config after benchmarking all related configs _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' data = dict(samples_per_gpu=4, workers_per_gpu=4)
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mmdetection
mmdetection-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py
_base_ = ['./fcos_r50_caffe_fpn_gn-head_1x_coco.py'] data = dict(samples_per_gpu=8, workers_per_gpu=8) # optimizer optimizer = dict(lr=0.04) fp16 = dict(loss_scale='dynamic') # learning policy # In order to avoid non-convergence in the early stage of # mixed-precision training, the warmup in the lr_config is set to linear, # warmup_iters increases and warmup_ratio decreases. lr_config = dict(warmup='linear', warmup_iters=1000, warmup_ratio=1.0 / 10)
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mmdetection
mmdetection-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' img_norm_cfg = dict( mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' model = dict( backbone=dict( type='ResNeXt', depth=101, groups=64, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # optimizer optimizer = dict( lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/fcos/metafile.yml
Collections: - Name: FCOS Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - FPN - Group Normalization - ResNet Paper: URL: https://arxiv.org/abs/1904.01355 Title: 'FCOS: Fully Convolutional One-Stage Object Detection' README: configs/fcos/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fcos.py#L6 Version: v2.0.0 Models: - Name: fcos_r50_caffe_fpn_gn-head_1x_coco In Collection: FCOS Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py Metadata: Training Memory (GB): 3.6 inference time (ms/im): - value: 44.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 36.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco In Collection: FCOS Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py Metadata: Training Memory (GB): 3.7 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco In Collection: FCOS Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py Metadata: Training Memory (GB): 3.8 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth - Name: fcos_r101_caffe_fpn_gn-head_1x_coco In Collection: FCOS Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py Metadata: Training Memory (GB): 5.5 inference time (ms/im): - value: 57.8 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth - Name: fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco In Collection: FCOS Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py Metadata: Training Memory (GB): 2.6 inference time (ms/im): - value: 43.67 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth - Name: fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco In Collection: FCOS Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py Metadata: Training Memory (GB): 5.5 inference time (ms/im): - value: 57.8 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth - Name: fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco In Collection: FCOS Config: configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py Metadata: Training Memory (GB): 10.0 inference time (ms/im): - value: 103.09 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth
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mmdetection
mmdetection-master/configs/foveabox/README.md
# FoveaBox > [FoveaBox: Beyond Anchor-based Object Detector](https://arxiv.org/abs/1904.03797) <!-- [ALGORITHM] --> ## Abstract We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143885497-332d38a7-b492-4f51-b9d2-ef9d4ad4412a.png"/> </div> ## Introduction FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797): Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. ## Results and Models ### Results on R50/101-FPN | Backbone | Style | align | ms-train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :------: | :-----: | :---: | :------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | | R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | | R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | | R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | | R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | | R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | | R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | | R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | \[1\] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \ \[2\] *Align means utilizing deformable convolution to align the cls branch.* \ \[3\] *All results are obtained with a single model and without any test time data augmentation.*\ \[4\] *We use 4 GPUs for training.* Any pull requests or issues are welcome. ## Citation Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows. ```latex @article{kong2019foveabox, title={FoveaBox: Beyond Anchor-based Object Detector}, author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo}, journal={arXiv preprint arXiv:1904.03797}, year={2019} } ```
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mmdetection
mmdetection-master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')), bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')), bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict(train=dict(pipeline=train_pipeline)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
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mmdetection
mmdetection-master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( bbox_head=dict( with_deform=True, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict(train=dict(pipeline=train_pipeline)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_2x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
_base_ = [ '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # model settings model = dict( type='FOVEA', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, num_outs=5, add_extra_convs='on_input'), bbox_head=dict( type='FoveaHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, strides=[8, 16, 32, 64, 128], base_edge_list=[16, 32, 64, 128, 256], scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), sigma=0.4, with_deform=False, loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=1.50, alpha=0.4, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), # training and testing settings train_cfg=dict(), test_cfg=dict( nms_pre=1000, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) data = dict(samples_per_gpu=4, workers_per_gpu=4) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
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mmdetection
mmdetection-master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
_base_ = './fovea_r50_fpn_4x4_1x_coco.py' # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/foveabox/metafile.yml
Collections: - Name: FoveaBox Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 4x V100 GPUs Architecture: - FPN - ResNet Paper: URL: https://arxiv.org/abs/1904.03797 Title: 'FoveaBox: Beyond Anchor-based Object Detector' README: configs/foveabox/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fovea.py#L6 Version: v2.0.0 Models: - Name: fovea_r50_fpn_4x4_1x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py Metadata: Training Memory (GB): 5.6 inference time (ms/im): - value: 41.49 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 36.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth - Name: fovea_r50_fpn_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py Metadata: Training Memory (GB): 5.6 inference time (ms/im): - value: 41.49 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py Metadata: Training Memory (GB): 8.1 inference time (ms/im): - value: 51.55 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py Metadata: Training Memory (GB): 8.1 inference time (ms/im): - value: 54.64 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth - Name: fovea_r101_fpn_4x4_1x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py Metadata: Training Memory (GB): 9.2 inference time (ms/im): - value: 57.47 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth - Name: fovea_r101_fpn_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py Metadata: Training Memory (GB): 11.7 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py Metadata: Training Memory (GB): 11.7 inference time (ms/im): - value: 68.03 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco In Collection: FoveaBox Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py Metadata: Training Memory (GB): 11.7 inference time (ms/im): - value: 68.03 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth
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mmdetection
mmdetection-master/configs/fpg/README.md
# FPG > [Feature Pyramid Grids](https://arxiv.org/abs/2004.03580) <!-- [ALGORITHM] --> ## Abstract Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143885611-85902399-2885-4a85-9126-9b9b7464ad08.png"/> </div> ## Results and Models We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. All backbones are Resnet-50 in pytorch style. | Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | Faster R-CNN | FPG | 50e | 20.0 | - | 42.3 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856.log.json) | | Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857.log.json) | | Faster R-CNN | FPN | 50e | 20.0 | - | 38.9 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857-be7c9f42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857.log.json) | | Mask R-CNN | FPG | 50e | 23.2 | - | 43.0 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857.log.json) | | Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859.log.json) | | Mask R-CNN | FPN | 50e | 23.2 | - | 39.6 | 35.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855-a756664a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855.log.json) | | RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809.log.json) | | RetinaNet | FPG-chn128 | 50e | 19.9 | - | 39.9 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829.log.json) | **Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. ## Citation ```latex @article{chen2020feature, title={Feature pyramid grids}, author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph}, journal={arXiv preprint arXiv:2004.03580}, year={2020} } ```
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mmdetection
mmdetection-master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
_base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py' norm_cfg = dict(type='BN', requires_grad=True) model = dict( neck=dict(out_channels=128, inter_channels=128), rpn_head=dict(in_channels=128), roi_head=dict( bbox_roi_extractor=dict(out_channels=128), bbox_head=dict(in_channels=128)))
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mmdetection
mmdetection-master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py' norm_cfg = dict(type='BN', requires_grad=True) model = dict( neck=dict( type='FPG', in_channels=[256, 512, 1024, 2048], out_channels=256, inter_channels=256, num_outs=5, stack_times=9, paths=['bu'] * 9, same_down_trans=None, same_up_trans=dict( type='conv', kernel_size=3, stride=2, padding=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_lateral_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_down_trans=dict( type='interpolation_conv', mode='nearest', kernel_size=3, norm_cfg=norm_cfg, order=('act', 'conv', 'norm'), inplace=False), across_up_trans=None, across_skip_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), output_trans=dict( type='last_conv', kernel_size=3, order=('act', 'conv', 'norm'), inplace=False), norm_cfg=norm_cfg, skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
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mmdetection
mmdetection-master/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py
_base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] norm_cfg = dict(type='BN', requires_grad=True) model = dict( backbone=dict(norm_cfg=norm_cfg, norm_eval=False), neck=dict(norm_cfg=norm_cfg), roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg))) dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict( type='Resize', img_scale=(640, 640), ratio_range=(0.8, 1.2), keep_ratio=True), dict(type='RandomCrop', crop_size=(640, 640)), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size=(640, 640)), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(640, 640), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=64), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=8, workers_per_gpu=4, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # learning policy optimizer = dict( type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001, paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.1, step=[30, 40]) # runtime settings runner = dict(max_epochs=50) evaluation = dict(interval=2) # NOTE: `auto_scale_lr` is for automatically scaling LR, # USER SHOULD NOT CHANGE ITS VALUES. # base_batch_size = (8 GPUs) x (8 samples per GPU) auto_scale_lr = dict(base_batch_size=64)
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mmdetection
mmdetection-master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
_base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py' model = dict( neck=dict(out_channels=128, inter_channels=128), rpn_head=dict(in_channels=128), roi_head=dict( bbox_roi_extractor=dict(out_channels=128), bbox_head=dict(in_channels=128), mask_roi_extractor=dict(out_channels=128), mask_head=dict(in_channels=128)))
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mmdetection
mmdetection-master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py
_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py' norm_cfg = dict(type='BN', requires_grad=True) model = dict( neck=dict( type='FPG', in_channels=[256, 512, 1024, 2048], out_channels=256, inter_channels=256, num_outs=5, stack_times=9, paths=['bu'] * 9, same_down_trans=None, same_up_trans=dict( type='conv', kernel_size=3, stride=2, padding=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_lateral_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_down_trans=dict( type='interpolation_conv', mode='nearest', kernel_size=3, norm_cfg=norm_cfg, order=('act', 'conv', 'norm'), inplace=False), across_up_trans=None, across_skip_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), output_trans=dict( type='last_conv', kernel_size=3, order=('act', 'conv', 'norm'), inplace=False), norm_cfg=norm_cfg, skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
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mmdetection
mmdetection-master/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py
_base_ = [ '../_base_/models/mask_rcnn_r50_fpn.py', '../_base_/datasets/coco_instance.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] norm_cfg = dict(type='BN', requires_grad=True) model = dict( backbone=dict(norm_cfg=norm_cfg, norm_eval=False), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, norm_cfg=norm_cfg, num_outs=5), roi_head=dict( bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict( type='Resize', img_scale=(640, 640), ratio_range=(0.8, 1.2), keep_ratio=True), dict(type='RandomCrop', crop_size=(640, 640)), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size=(640, 640)), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(640, 640), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=64), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=8, workers_per_gpu=4, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # learning policy optimizer = dict( type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001, paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.1, step=[30, 40]) # runtime settings runner = dict(max_epochs=50) evaluation = dict(interval=2) # NOTE: `auto_scale_lr` is for automatically scaling LR, # USER SHOULD NOT CHANGE ITS VALUES. # base_batch_size = (8 GPUs) x (8 samples per GPU) auto_scale_lr = dict(base_batch_size=64)
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mmdetection
mmdetection-master/configs/fpg/metafile.yml
Collections: - Name: Feature Pyramid Grids Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - Feature Pyramid Grids Paper: URL: https://arxiv.org/abs/2004.03580 Title: 'Feature Pyramid Grids' README: configs/fpg/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.10.0/mmdet/models/necks/fpg.py#L101 Version: v2.10.0 Models: - Name: faster_rcnn_r50_fpg_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py Metadata: Training Memory (GB): 20.0 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth - Name: faster_rcnn_r50_fpg-chn128_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py Metadata: Training Memory (GB): 11.9 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth - Name: mask_rcnn_r50_fpg_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py Metadata: Training Memory (GB): 23.2 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 43.0 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth - Name: mask_rcnn_r50_fpg-chn128_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py Metadata: Training Memory (GB): 15.3 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.7 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth - Name: retinanet_r50_fpg_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py Metadata: Training Memory (GB): 20.8 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth - Name: retinanet_r50_fpg-chn128_crop640_50e_coco In Collection: Feature Pyramid Grids Config: configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py Metadata: Training Memory (GB): 19.9 Epochs: 50 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth
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mmdetection
mmdetection-master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' model = dict( neck=dict(out_channels=128, inter_channels=128), bbox_head=dict(in_channels=128))
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mmdetection
mmdetection-master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' norm_cfg = dict(type='BN', requires_grad=True) model = dict( neck=dict( _delete_=True, type='FPG', in_channels=[256, 512, 1024, 2048], out_channels=256, inter_channels=256, num_outs=5, add_extra_convs=True, start_level=1, stack_times=9, paths=['bu'] * 9, same_down_trans=None, same_up_trans=dict( type='conv', kernel_size=3, stride=2, padding=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_lateral_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), across_down_trans=dict( type='interpolation_conv', mode='nearest', kernel_size=3, norm_cfg=norm_cfg, order=('act', 'conv', 'norm'), inplace=False), across_up_trans=None, across_skip_trans=dict( type='conv', kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=('act', 'conv', 'norm')), output_trans=dict( type='last_conv', kernel_size=3, order=('act', 'conv', 'norm'), inplace=False), norm_cfg=norm_cfg, skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) evaluation = dict(interval=2)
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mmdetection
mmdetection-master/configs/free_anchor/README.md
# FreeAnchor > [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://arxiv.org/abs/1909.02466) <!-- [ALGORITHM] --> ## Abstract Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143886006-8374bf9b-28af-442e-9abf-eb16562bb7d3.png"/> </div> ## Results and Models | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :---------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | | R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | | X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | **Notes:** - We use 8 GPUs with 2 images/GPU. - For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor). ## Citation ```latex @inproceedings{zhang2019freeanchor, title = {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection}, author = {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang}, booktitle = {Neural Information Processing Systems}, year = {2019} } ```
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mmdetection
mmdetection-master/configs/free_anchor/metafile.yml
Collections: - Name: FreeAnchor Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - FreeAnchor - ResNet Paper: URL: https://arxiv.org/abs/1909.02466 Title: 'FreeAnchor: Learning to Match Anchors for Visual Object Detection' README: configs/free_anchor/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/free_anchor_retina_head.py#L10 Version: v2.0.0 Models: - Name: retinanet_free_anchor_r50_fpn_1x_coco In Collection: FreeAnchor Config: configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py Metadata: Training Memory (GB): 4.9 inference time (ms/im): - value: 54.35 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth - Name: retinanet_free_anchor_r101_fpn_1x_coco In Collection: FreeAnchor Config: configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py Metadata: Training Memory (GB): 6.8 inference time (ms/im): - value: 67.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth - Name: retinanet_free_anchor_x101_32x4d_fpn_1x_coco In Collection: FreeAnchor Config: configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 8.1 inference time (ms/im): - value: 90.09 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth
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32.1125
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yml
mmdetection
mmdetection-master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py
_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' model = dict( bbox_head=dict( _delete_=True, type='FreeAnchorRetinaHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2]), loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
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mmdetection
mmdetection-master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' model = dict( backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
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mmdetection-master/configs/fsaf/README.md
# FSAF > [Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://arxiv.org/abs/1903.00621) <!-- [ALGORITHM] --> ## Abstract We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143887228-c790b542-c0f7-4113-8597-12250c50fe8f.png"/> </div> ## Introduction FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. And this is how we implemented it. Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget. In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However, it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below) ## Results and Models ### Results on R50/R101/X101-FPN | Backbone | ignore range | ms-train | Lr schd | Train Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Config | Download | | :------: | :----------: | :------: | :-----: | :------------: | :-----------------: | :------------: | :---------: | :---------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | | R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json) | | R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json) | | X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json) | **Notes:** - *1x means the model is trained for 12 epochs.* - *AP values in the brackets represent those reported in the original paper.* - *All results are obtained with a single model and single-scale test.* - *X-101 backbone represents ResNext-101-64x4d.* - *All pretrained backbones use pytorch style.* - *All models are trained on 8 Titan-XP gpus and tested on a single gpu.* ## Citation BibTeX reference is as follows. ```latex @inproceedings{zhu2019feature, title={Feature Selective Anchor-Free Module for Single-Shot Object Detection}, author={Zhu, Chenchen and He, Yihui and Savvides, Marios}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={840--849}, year={2019} } ```
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mmdetection
mmdetection-master/configs/fsaf/fsaf_r101_fpn_1x_coco.py
_base_ = './fsaf_r50_fpn_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/fsaf/fsaf_r50_fpn_1x_coco.py
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' # model settings model = dict( type='FSAF', bbox_head=dict( type='FSAFHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, reg_decoded_bbox=True, # Only anchor-free branch is implemented. The anchor generator only # generates 1 anchor at each feature point, as a substitute of the # grid of features. anchor_generator=dict( type='AnchorGenerator', octave_base_scale=1, scales_per_octave=1, ratios=[1.0], strides=[8, 16, 32, 64, 128]), bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, reduction='none'), loss_bbox=dict( _delete_=True, type='IoULoss', eps=1e-6, loss_weight=1.0, reduction='none')), # training and testing settings train_cfg=dict( assigner=dict( _delete_=True, type='CenterRegionAssigner', pos_scale=0.2, neg_scale=0.2, min_pos_iof=0.01), allowed_border=-1, pos_weight=-1, debug=False)) optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
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mmdetection
mmdetection-master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py
_base_ = './fsaf_r50_fpn_1x_coco.py' model = dict( backbone=dict( type='ResNeXt', depth=101, groups=64, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
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mmdetection
mmdetection-master/configs/fsaf/metafile.yml
Collections: - Name: FSAF Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x Titan-XP GPUs Architecture: - FPN - FSAF - ResNet Paper: URL: https://arxiv.org/abs/1903.00621 Title: 'Feature Selective Anchor-Free Module for Single-Shot Object Detection' README: configs/fsaf/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/fsaf.py#L6 Version: v2.1.0 Models: - Name: fsaf_r50_fpn_1x_coco In Collection: FSAF Config: configs/fsaf/fsaf_r50_fpn_1x_coco.py Metadata: Training Memory (GB): 3.15 inference time (ms/im): - value: 76.92 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth - Name: fsaf_r101_fpn_1x_coco In Collection: FSAF Config: configs/fsaf/fsaf_r101_fpn_1x_coco.py Metadata: Training Memory (GB): 5.08 inference time (ms/im): - value: 92.59 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.3 (37.9) Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth - Name: fsaf_x101_64x4d_fpn_1x_coco In Collection: FSAF Config: configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 9.38 inference time (ms/im): - value: 178.57 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.4 (41.0) Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth
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mmdetection
mmdetection-master/configs/gcnet/README.md
# GCNet > [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492) <!-- [ALGORITHM] --> ## Abstract The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143887418-c5d2c906-d6c9-415d-bce7-c5355c20a10a.png" height="300"/> </div> ## Introduction By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). We provide config files to reproduce the results in the paper for ["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. **GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. ## Results and Models The results on COCO 2017val are shown in the below table. | Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------: | :---: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | | R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | | R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | | R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | | Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | | R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | | R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | | R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | | R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | | R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | | X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | | X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | | X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | | X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | | X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | | X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | | X-101-FPN | DCN Cascade Mask | - | 1x | | | 47.5 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019.log.json) | | X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 48.0 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648.log.json) | | X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 47.9 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851.log.json) | **Notes:** - The `SyncBN` is added in the backbone for all models in **Table 2**. - `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. - `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. - `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively. ## Citation ```latex @article{cao2019GCNet, title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, journal={arXiv preprint arXiv:1904.11492}, year={2019} } ```
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
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mmdetection
mmdetection-master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
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py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' model = dict( backbone=dict(plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
258
27.777778
57
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' model = dict( backbone=dict(plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
257
27.666667
56
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
163
31.8
75
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
370
29.916667
61
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
369
29.833333
60
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( backbone=dict(plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
257
27.666667
57
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( backbone=dict(plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
256
27.555556
56
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
162
31.6
75
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
369
29.833333
61
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
368
29.75
60
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
169
33
75
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 16), stages=(False, True, True, True), position='after_conv3') ]))
376
30.416667
61
py
mmdetection
mmdetection-master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' model = dict( backbone=dict( norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, plugins=[ dict( cfg=dict(type='ContextBlock', ratio=1. / 4), stages=(False, True, True, True), position='after_conv3') ]))
375
30.333333
60
py
mmdetection
mmdetection-master/configs/gcnet/metafile.yml
Collections: - Name: GCNet Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - Global Context Block - FPN - RPN - ResNet - ResNeXt Paper: URL: https://arxiv.org/abs/1904.11492 Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' README: configs/gcnet/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13 Version: v2.0.0 Models: - Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 5.0 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.7 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 35.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth - Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 5.1 inference time (ms/im): - value: 66.67 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.9 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth - Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 7.6 inference time (ms/im): - value: 87.72 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.3 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth - Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 7.8 inference time (ms/im): - value: 86.21 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.2 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth - Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py Metadata: Training Memory (GB): 4.4 inference time (ms/im): - value: 60.24 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 38.4 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 34.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth - Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 5.0 inference time (ms/im): - value: 64.52 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.4 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 5.1 inference time (ms/im): - value: 66.23 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.7 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth - Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py Metadata: Training Memory (GB): 6.4 inference time (ms/im): - value: 75.19 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.5 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth - Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 7.6 inference time (ms/im): - value: 83.33 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.2 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 7.8 inference time (ms/im): - value: 84.75 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.2 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.8 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py Metadata: Training Memory (GB): 7.6 inference time (ms/im): - value: 88.5 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.4 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 8.8 inference time (ms/im): - value: 102.04 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 43.5 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 9.0 inference time (ms/im): - value: 103.09 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 43.9 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 39.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py Metadata: Training Memory (GB): 9.2 inference time (ms/im): - value: 119.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 44.7 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 10.3 inference time (ms/im): - value: 129.87 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 46.2 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 39.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py Metadata: Training Memory (GB): 10.6 Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 46.4 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 40.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 47.5 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 40.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 48.0 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 41.3 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco In Collection: GCNet Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py Metadata: Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 47.9 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 41.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth
15,471
34.0839
261
yml
mmdetection
mmdetection-master/configs/gfl/README.md
# GFL > [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) <!-- [ALGORITHM] --> ## Abstract One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5%) and ATSS (43.6%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2%, at 10 FPS on a single 2080Ti GPU. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143887865-44dc384d-ba0d-44e8-b3d7-d5fa837838cf.png"/> </div> ## Results and Models | Backbone | Style | Lr schd | Multi-scale Training | Inf time (fps) | box AP | Config | Download | | :---------------: | :-----: | :-----: | :------------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | | R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | | R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | | R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | | X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | | X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | \[1\] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \ \[2\] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \ \[3\] *`dcnv2` denotes deformable convolutional networks v2.* \ \[4\] *FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.* ## Citation We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) ```latex @article{li2020generalized, title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection}, author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian}, journal={arXiv preprint arXiv:2006.04388}, year={2020} } ```
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mmdetection
mmdetection-master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( backbone=dict( type='ResNet', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, True, True, True), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( backbone=dict( type='ResNet', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/gfl/gfl_r50_fpn_1x_coco.py
_base_ = [ '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( type='GFL', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_output', num_outs=5), bbox_head=dict( type='GFLHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], octave_base_scale=8, scales_per_octave=1, strides=[8, 16, 32, 64, 128]), loss_cls=dict( type='QualityFocalLoss', use_sigmoid=True, beta=2.0, loss_weight=1.0), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), # training and testing settings train_cfg=dict( assigner=dict(type='ATSSAssigner', topk=9), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
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mmdetection
mmdetection-master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
_base_ = './gfl_r50_fpn_1x_coco.py' # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24) # multi-scale training img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict( type='Resize', img_scale=[(1333, 480), (1333, 800)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict(train=dict(pipeline=train_pipeline))
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mmdetection
mmdetection-master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( type='GFL', backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), stage_with_dcn=(False, False, True, True), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
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mmdetection
mmdetection-master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' model = dict( type='GFL', backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
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mmdetection
mmdetection-master/configs/gfl/metafile.yml
Collections: - Name: Generalized Focal Loss Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - Generalized Focal Loss - FPN - ResNet Paper: URL: https://arxiv.org/abs/2006.04388 Title: 'Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection' README: configs/gfl/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/gfl.py#L6 Version: v2.2.0 Models: - Name: gfl_r50_fpn_1x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r50_fpn_1x_coco.py Metadata: inference time (ms/im): - value: 51.28 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth - Name: gfl_r50_fpn_mstrain_2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py Metadata: inference time (ms/im): - value: 51.28 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth - Name: gfl_r101_fpn_mstrain_2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py Metadata: inference time (ms/im): - value: 68.03 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 44.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth - Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py Metadata: inference time (ms/im): - value: 77.52 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 47.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth - Name: gfl_x101_32x4d_fpn_mstrain_2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py Metadata: inference time (ms/im): - value: 82.64 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 45.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth - Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco In Collection: Generalized Focal Loss Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py Metadata: inference time (ms/im): - value: 93.46 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 48.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth
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mmdetection
mmdetection-master/configs/ghm/README.md
# GHM > [Gradient Harmonized Single-stage Detector](https://arxiv.org/abs/1811.05181) <!-- [ALGORITHM] --> ## Abstract Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i.e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples. In this work, we first point out that the essential effect of the two disharmonies can be summarized in term of the gradient. Further, we propose a novel gradient harmonizing mechanism (GHM) to be a hedging for the disharmonies. The philosophy behind GHM can be easily embedded into both classification loss function like cross-entropy (CE) and regression loss function like smooth-L1 (SL1) loss. To this end, two novel loss functions called GHM-C and GHM-R are designed to balancing the gradient flow for anchor classification and bounding box refinement, respectively. Ablation study on MS COCO demonstrates that without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring substantial improvement for single-stage detector. Without any whistles and bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the state-of-the-art method, Focal Loss (FL) + SL1, by 0.8. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143889057-0341f32b-1291-4b9a-8444-52ad266ae709.png"/> </div> ## Results and Models | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | | R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | | X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | | X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | ## Citation ```latex @inproceedings{li2019gradient, title={Gradient Harmonized Single-stage Detector}, author={Li, Buyu and Liu, Yu and Wang, Xiaogang}, booktitle={AAAI Conference on Artificial Intelligence}, year={2019} } ```
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mmdetection
mmdetection-master/configs/ghm/metafile.yml
Collections: - Name: GHM Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - GHM-C - GHM-R - FPN - ResNet Paper: URL: https://arxiv.org/abs/1811.05181 Title: 'Gradient Harmonized Single-stage Detector' README: configs/ghm/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/losses/ghm_loss.py#L21 Version: v2.0.0 Models: - Name: retinanet_ghm_r50_fpn_1x_coco In Collection: GHM Config: configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py Metadata: Training Memory (GB): 4.0 inference time (ms/im): - value: 303.03 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 37.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth - Name: retinanet_ghm_r101_fpn_1x_coco In Collection: GHM Config: configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py Metadata: Training Memory (GB): 6.0 inference time (ms/im): - value: 227.27 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth - Name: retinanet_ghm_x101_32x4d_fpn_1x_coco In Collection: GHM Config: configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 7.2 inference time (ms/im): - value: 196.08 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth - Name: retinanet_ghm_x101_64x4d_fpn_1x_coco In Collection: GHM Config: configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py Metadata: Training Memory (GB): 10.3 inference time (ms/im): - value: 192.31 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth
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mmdetection
mmdetection-master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py
_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
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mmdetection
mmdetection-master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' model = dict( bbox_head=dict( loss_cls=dict( _delete_=True, type='GHMC', bins=30, momentum=0.75, use_sigmoid=True, loss_weight=1.0), loss_bbox=dict( _delete_=True, type='GHMR', mu=0.02, bins=10, momentum=0.7, loss_weight=10.0))) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
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25.65
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py
mmdetection
mmdetection-master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py
_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' model = dict( backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
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mmdetection
mmdetection-master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py
_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' model = dict( backbone=dict( type='ResNeXt', depth=101, groups=64, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
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mmdetection
mmdetection-master/configs/gn+ws/README.md
# GN + WS > [Weight Standardization](https://arxiv.org/abs/1903.10520) <!-- [ALGORITHM] --> ## Abstract Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143889309-b1f3487d-abfe-4615-befc-79384bc8b65c.png"/ height="200"> </div> ## Results and Models Faster R-CNN | Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | | R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | | X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | | X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | Mask R-CNN | Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-------------: | :-----: | :-----------: | :-------: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | | R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | | X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | | X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | | R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | | R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | | X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | | X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | Note: - GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN. - In the paper, a 20-23-24e lr schedule is used instead of 2x. - The X-50-GN and X-101-GN pretrained models are also shared by the authors. ## Citation ```latex @article{weightstandardization, author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille}, title = {Weight Standardization}, journal = {arXiv preprint arXiv:1903.10520}, year = {2019}, } ```
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md
mmdetection
mmdetection-master/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
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mmdetection
mmdetection-master/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', conv_out_channels=256, conv_cfg=conv_cfg, norm_cfg=norm_cfg)))
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mmdetection
mmdetection-master/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws')))
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mmdetection
mmdetection-master/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( type='ResNeXt', depth=50, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws')))
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
_base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' # learning policy lr_config = dict(step=[20, 23]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
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py
mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' # learning policy lr_config = dict(step=[20, 23]) runner = dict(type='EpochBasedRunner', max_epochs=24)
156
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', conv_out_channels=256, conv_cfg=conv_cfg, norm_cfg=norm_cfg), mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg))) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' # learning policy lr_config = dict(step=[20, 23]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' # model settings conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( type='ResNeXt', depth=101, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws')))
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' # learning policy lr_config = dict(step=[20, 23]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' # model settings conv_cfg = dict(type='ConvWS') norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( type='ResNeXt', depth=50, groups=32, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', conv_cfg=conv_cfg, norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws')))
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mmdetection
mmdetection-master/configs/gn+ws/metafile.yml
Collections: - Name: Weight Standardization Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - Group Normalization - Weight Standardization Paper: URL: https://arxiv.org/abs/1903.10520 Title: 'Weight Standardization' README: configs/gn+ws/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py Version: v2.0.0 Models: - Name: faster_rcnn_r50_fpn_gn_ws-all_1x_coco In Collection: Weight Standardization Config: configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py Metadata: Training Memory (GB): 5.9 inference time (ms/im): - value: 85.47 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 39.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth - Name: faster_rcnn_r101_fpn_gn_ws-all_1x_coco In Collection: Weight Standardization Config: configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py Metadata: Training Memory (GB): 8.9 inference time (ms/im): - value: 111.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth - Name: faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco In Collection: Weight Standardization Config: configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py Metadata: Training Memory (GB): 7.0 inference time (ms/im): - value: 97.09 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth - Name: faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco In Collection: Weight Standardization Config: configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py Metadata: Training Memory (GB): 10.8 inference time (ms/im): - value: 131.58 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 12 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth - Name: mask_rcnn_r50_fpn_gn_ws-all_2x_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py Metadata: Training Memory (GB): 7.3 inference time (ms/im): - value: 95.24 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.6 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth - Name: mask_rcnn_r101_fpn_gn_ws-all_2x_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py Metadata: Training Memory (GB): 10.3 inference time (ms/im): - value: 116.28 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.0 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py Metadata: Training Memory (GB): 8.4 inference time (ms/im): - value: 107.53 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py Metadata: Training Memory (GB): 12.2 inference time (ms/im): - value: 140.85 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.9 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth - Name: mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py Metadata: Training Memory (GB): 7.3 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth - Name: mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py Metadata: Training Memory (GB): 10.3 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 43.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py Metadata: Training Memory (GB): 8.4 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco In Collection: Weight Standardization Config: configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py Metadata: Training Memory (GB): 12.2 Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.7 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.5 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth
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mmdetection
mmdetection-master/configs/gn/README.md
# GN > [Group Normalization](https://arxiv.org/abs/1803.08494) <!-- [ALGORITHM] --> ## Abstract Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries. <div align=center> <img src="https://user-images.githubusercontent.com/40661020/143889171-d4660307-3a4f-4ac2-a0ad-7d17c2f045b1.png" height="200"/> </div> ## Results and Models | Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | | :-----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | | R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | | R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | | R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | | R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | | R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | **Notes:** - (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). - The `3x` schedule is epoch \[28, 34, 36\]. - **Memory, Train/Inf time is outdated.** ## Citation ```latex @inproceedings{wu2018group, title={Group Normalization}, author={Wu, Yuxin and He, Kaiming}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, year={2018} } ```
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mmdetection
mmdetection-master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' model = dict( backbone=dict( depth=101, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron/resnet101_gn')))
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mmdetection-master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py
_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' # learning policy lr_config = dict(step=[28, 34]) runner = dict(type='EpochBasedRunner', max_epochs=36)
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mmdetection
mmdetection-master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://detectron/resnet50_gn')), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', conv_out_channels=256, norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' # learning policy lr_config = dict(step=[28, 34]) runner = dict(type='EpochBasedRunner', max_epochs=36)
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mmdetection
mmdetection-master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) model = dict( backbone=dict( norm_cfg=norm_cfg, init_cfg=dict( type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')), neck=dict(norm_cfg=norm_cfg), roi_head=dict( bbox_head=dict( type='Shared4Conv1FCBBoxHead', conv_out_channels=256, norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) # learning policy lr_config = dict(step=[16, 22]) runner = dict(type='EpochBasedRunner', max_epochs=24)
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mmdetection
mmdetection-master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
_base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' # learning policy lr_config = dict(step=[28, 34]) runner = dict(type='EpochBasedRunner', max_epochs=36)
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mmdetection
mmdetection-master/configs/gn/metafile.yml
Collections: - Name: Group Normalization Metadata: Training Data: COCO Training Techniques: - SGD with Momentum - Weight Decay Training Resources: 8x V100 GPUs Architecture: - Group Normalization Paper: URL: https://arxiv.org/abs/1803.08494 Title: 'Group Normalization' README: configs/gn/README.md Code: URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py Version: v2.0.0 Models: - Name: mask_rcnn_r50_fpn_gn-all_2x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py Metadata: Training Memory (GB): 7.1 inference time (ms/im): - value: 90.91 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.2 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.4 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth - Name: mask_rcnn_r50_fpn_gn-all_3x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py Metadata: Training Memory (GB): 7.1 inference time (ms/im): - value: 90.91 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.5 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.7 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth - Name: mask_rcnn_r101_fpn_gn-all_2x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py Metadata: Training Memory (GB): 9.9 inference time (ms/im): - value: 111.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 41.9 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 37.6 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth - Name: mask_rcnn_r101_fpn_gn-all_3x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py Metadata: Training Memory (GB): 9.9 inference time (ms/im): - value: 111.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 42.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 38.0 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth - Name: mask_rcnn_r50_fpn_gn-all_contrib_2x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py Metadata: Training Memory (GB): 7.1 inference time (ms/im): - value: 91.74 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 24 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.0 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.1 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth - Name: mask_rcnn_r50_fpn_gn-all_contrib_3x_coco In Collection: Group Normalization Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py Metadata: Training Memory (GB): 7.1 inference time (ms/im): - value: 91.74 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (800, 1333) Epochs: 36 Results: - Task: Object Detection Dataset: COCO Metrics: box AP: 40.1 - Task: Instance Segmentation Dataset: COCO Metrics: mask AP: 36.2 Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth
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