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segformer-b0-finetuned-segments-sidewalk-test

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  1. README.md +53 -95
  2. config.json +4 -70
  3. model.safetensors +2 -2
  4. runs/May26_22-08-47_c62e72524a85/events.out.tfevents.1716761332.c62e72524a85.903.9 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -1,10 +1,10 @@
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  ---
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  license: other
 
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  tags:
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  - vision
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  - image-segmentation
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  - generated_from_trainer
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- base_model: nvidia/mit-b0
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  model-index:
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  - name: segformer-b0-finetuned-segments-sidewalk-test
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  results: []
@@ -17,80 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4005
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- - Mean Iou: 0.1493
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- - Mean Accuracy: 0.1996
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- - Overall Accuracy: 0.7190
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- - Accuracy Unlabeled: nan
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- - Accuracy Flat-road: 0.8283
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- - Accuracy Flat-sidewalk: 0.9194
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- - Accuracy Flat-crosswalk: 0.0
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- - Accuracy Flat-cyclinglane: 0.3028
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- - Accuracy Flat-parkingdriveway: 0.0018
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- - Accuracy Flat-railtrack: nan
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- - Accuracy Flat-curb: 0.0
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- - Accuracy Human-person: 0.0
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- - Accuracy Human-rider: 0.0
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- - Accuracy Vehicle-car: 0.8471
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- - Accuracy Vehicle-truck: 0.0
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- - Accuracy Vehicle-bus: 0.0
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- - Accuracy Vehicle-tramtrain: 0.0
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- - Accuracy Vehicle-motorcycle: 0.0
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- - Accuracy Vehicle-bicycle: 0.0
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- - Accuracy Vehicle-caravan: 0.0
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- - Accuracy Vehicle-cartrailer: 0.0
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- - Accuracy Construction-building: 0.8710
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- - Accuracy Construction-door: 0.0
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- - Accuracy Construction-wall: 0.0000
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- - Accuracy Construction-fenceguardrail: 0.0
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- - Accuracy Construction-bridge: 0.0
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- - Accuracy Construction-tunnel: nan
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- - Accuracy Construction-stairs: 0.0
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- - Accuracy Object-pole: 0.0
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- - Accuracy Object-trafficsign: 0.0
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- - Accuracy Object-trafficlight: 0.0
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- - Accuracy Nature-vegetation: 0.9276
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- - Accuracy Nature-terrain: 0.7787
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- - Accuracy Sky: 0.9107
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- - Accuracy Void-ground: 0.0
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- - Accuracy Void-dynamic: 0.0
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- - Accuracy Void-static: 0.0
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- - Accuracy Void-unclear: 0.0
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- - Iou Unlabeled: nan
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- - Iou Flat-road: 0.4643
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- - Iou Flat-sidewalk: 0.7689
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- - Iou Flat-crosswalk: 0.0
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- - Iou Flat-cyclinglane: 0.2893
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- - Iou Flat-parkingdriveway: 0.0018
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- - Iou Flat-railtrack: nan
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- - Iou Flat-curb: 0.0
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- - Iou Human-person: 0.0
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- - Iou Human-rider: 0.0
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- - Iou Vehicle-car: 0.5408
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- - Iou Vehicle-truck: 0.0
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- - Iou Vehicle-bus: 0.0
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- - Iou Vehicle-tramtrain: 0.0
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- - Iou Vehicle-motorcycle: 0.0
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- - Iou Vehicle-bicycle: 0.0
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- - Iou Vehicle-caravan: 0.0
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- - Iou Vehicle-cartrailer: 0.0
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- - Iou Construction-building: 0.5284
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- - Iou Construction-door: 0.0
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- - Iou Construction-wall: 0.0000
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- - Iou Construction-fenceguardrail: 0.0
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- - Iou Construction-bridge: 0.0
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- - Iou Construction-tunnel: nan
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- - Iou Construction-stairs: 0.0
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- - Iou Object-pole: 0.0
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- - Iou Object-trafficsign: 0.0
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- - Iou Object-trafficlight: 0.0
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- - Iou Nature-vegetation: 0.7388
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- - Iou Nature-terrain: 0.6219
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- - Iou Sky: 0.8235
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- - Iou Void-ground: 0.0
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- - Iou Void-dynamic: 0.0
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- - Iou Void-static: 0.0
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- - Iou Void-unclear: 0.0
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 6e-05
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- - train_batch_size: 5
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- - eval_batch_size: 5
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
@@ -119,24 +53,48 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
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- | 2.9265 | 0.125 | 20 | 3.1813 | 0.0768 | 0.1312 | 0.5632 | nan | 0.5603 | 0.7775 | 0.0 | 0.0140 | 0.0014 | nan | 0.0029 | 0.0 | 0.0 | 0.4196 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8721 | 0.0001 | 0.0036 | 0.0 | 0.0 | nan | 0.0 | 0.0043 | 0.0 | 0.0 | 0.8901 | 0.0000 | 0.6515 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.3073 | 0.6151 | 0.0 | 0.0136 | 0.0014 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.2881 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3574 | 0.0001 | 0.0035 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0031 | 0.0 | 0.0 | 0.5179 | 0.0000 | 0.5764 | 0.0 | 0.0 | 0.0000 | 0.0 |
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- | 2.3514 | 0.25 | 40 | 2.4438 | 0.0897 | 0.1416 | 0.6137 | nan | 0.5554 | 0.8879 | 0.0 | 0.0018 | 0.0003 | nan | 0.0001 | 0.0 | 0.0 | 0.4850 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8892 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0003 | 0.0 | 0.0 | 0.9324 | 0.0353 | 0.7424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3608 | 0.6576 | 0.0 | 0.0018 | 0.0003 | nan | 0.0001 | 0.0 | 0.0 | 0.3424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3953 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.6036 | 0.0328 | 0.6550 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 2.1661 | 0.375 | 60 | 2.0340 | 0.1021 | 0.1527 | 0.6343 | nan | 0.6666 | 0.8877 | 0.0 | 0.0002 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.6564 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8405 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.9569 | 0.0105 | 0.8666 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3875 | 0.6740 | 0.0 | 0.0002 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.4746 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4621 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.5806 | 0.0103 | 0.7778 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.7349 | 0.5 | 80 | 1.9382 | 0.1105 | 0.1575 | 0.6438 | nan | 0.7397 | 0.8788 | 0.0 | 0.0002 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.6543 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8922 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9392 | 0.0817 | 0.8538 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3925 | 0.6929 | 0.0 | 0.0002 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.4912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4611 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6222 | 0.0774 | 0.7963 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.924 | 0.625 | 100 | 1.8056 | 0.1157 | 0.1670 | 0.6575 | nan | 0.7793 | 0.8856 | 0.0 | 0.0005 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8030 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8564 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9320 | 0.1652 | 0.9215 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4018 | 0.7082 | 0.0 | 0.0005 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.5214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4969 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6389 | 0.1544 | 0.7809 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.7874 | 0.75 | 120 | 1.7087 | 0.1221 | 0.1719 | 0.6708 | nan | 0.7656 | 0.9101 | 0.0 | 0.0090 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.8158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8509 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9381 | 0.3056 | 0.9058 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4260 | 0.7126 | 0.0 | 0.0090 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.5112 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5032 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6619 | 0.2741 | 0.8081 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.6627 | 0.875 | 140 | 1.6229 | 0.1311 | 0.1827 | 0.6844 | nan | 0.7938 | 0.9075 | 0.0 | 0.0283 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8237 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9327 | 0.5802 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4219 | 0.7271 | 0.0 | 0.0282 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.4897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5121 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7075 | 0.5019 | 0.8048 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.8796 | 1.0 | 160 | 1.5758 | 0.1395 | 0.1895 | 0.6997 | nan | 0.7653 | 0.9233 | 0.0 | 0.1513 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8741 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9194 | 0.7242 | 0.9105 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4533 | 0.7331 | 0.0 | 0.1488 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.5301 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5018 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7250 | 0.5601 | 0.8116 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.6484 | 1.125 | 180 | 1.4754 | 0.1364 | 0.1882 | 0.6932 | nan | 0.8187 | 0.9058 | 0.0 | 0.0299 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8469 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9242 | 0.7270 | 0.8980 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4198 | 0.7416 | 0.0 | 0.0299 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.5071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5276 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7306 | 0.5894 | 0.8186 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.4095 | 1.25 | 200 | 1.4997 | 0.1385 | 0.1904 | 0.7014 | nan | 0.7921 | 0.9185 | 0.0 | 0.1931 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.8786 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8072 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9514 | 0.6404 | 0.9100 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4564 | 0.7473 | 0.0 | 0.1888 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.4816 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5331 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6946 | 0.5171 | 0.8120 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.4449 | 1.375 | 220 | 1.4482 | 0.1469 | 0.1964 | 0.7137 | nan | 0.7940 | 0.9302 | 0.0 | 0.2830 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.8043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8714 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9086 | 0.7702 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4816 | 0.7429 | 0.0 | 0.2700 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.5463 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5141 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7348 | 0.5993 | 0.8096 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.5214 | 1.5 | 240 | 1.4310 | 0.1469 | 0.1982 | 0.7145 | nan | 0.8203 | 0.9197 | 0.0 | 0.2729 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.8678 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8428 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9264 | 0.7713 | 0.9180 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4646 | 0.7557 | 0.0 | 0.2621 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.5194 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5345 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7322 | 0.6126 | 0.8190 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.3618 | 1.625 | 260 | 1.3869 | 0.1478 | 0.1970 | 0.7178 | nan | 0.8004 | 0.9370 | 0.0 | 0.2759 | 0.0014 | nan | 0.0 | 0.0 | 0.0 | 0.8525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8705 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9219 | 0.7241 | 0.9202 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4847 | 0.7518 | 0.0 | 0.2650 | 0.0014 | nan | 0.0 | 0.0 | 0.0 | 0.5336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5236 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7412 | 0.6080 | 0.8211 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.34 | 1.75 | 280 | 1.3815 | 0.1493 | 0.1990 | 0.7202 | nan | 0.8029 | 0.9383 | 0.0 | 0.2942 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.8613 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8639 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9140 | 0.7655 | 0.9265 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4841 | 0.7544 | 0.0 | 0.2808 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.5342 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5267 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7461 | 0.6309 | 0.8190 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.312 | 1.875 | 300 | 1.3870 | 0.1491 | 0.1985 | 0.7172 | nan | 0.8410 | 0.9152 | 0.0 | 0.3010 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8252 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8523 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9377 | 0.7589 | 0.9195 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4533 | 0.7708 | 0.0 | 0.2872 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.5605 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5299 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7287 | 0.6193 | 0.8202 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.2746 | 2.0 | 320 | 1.4005 | 0.1493 | 0.1996 | 0.7190 | nan | 0.8283 | 0.9194 | 0.0 | 0.3028 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.8471 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8710 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9276 | 0.7787 | 0.9107 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4643 | 0.7689 | 0.0 | 0.2893 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.5408 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5284 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7388 | 0.6219 | 0.8235 | 0.0 | 0.0 | 0.0 | 0.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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  license: other
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+ base_model: nvidia/mit-b0
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  tags:
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  - vision
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  - image-segmentation
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  - generated_from_trainer
 
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  model-index:
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  - name: segformer-b0-finetuned-segments-sidewalk-test
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  results: []
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1893
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+ - Mean Iou: 0.4552
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+ - Mean Accuracy: 0.9104
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+ - Overall Accuracy: 0.9104
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+ - Accuracy Other: nan
25
+ - Accuracy Flat-sidewalk: 0.9104
26
+ - Iou Other: 0.0
27
+ - Iou Flat-sidewalk: 0.9104
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
28
 
29
  ## Model description
30
 
 
44
 
45
  The following hyperparameters were used during training:
46
  - learning_rate: 6e-05
47
+ - train_batch_size: 2
48
+ - eval_batch_size: 2
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
 
53
 
54
  ### Training results
55
 
56
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Other | Accuracy Flat-sidewalk | Iou Other | Iou Flat-sidewalk |
57
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:----------------------:|:---------:|:-----------------:|
58
+ | 0.4805 | 0.05 | 20 | 0.5080 | 0.4534 | 0.9069 | 0.9069 | nan | 0.9069 | 0.0 | 0.9069 |
59
+ | 0.2146 | 0.1 | 40 | 0.3937 | 0.4660 | 0.9319 | 0.9319 | nan | 0.9319 | 0.0 | 0.9319 |
60
+ | 0.215 | 0.15 | 60 | 0.3593 | 0.4476 | 0.8952 | 0.8952 | nan | 0.8952 | 0.0 | 0.8952 |
61
+ | 0.151 | 0.2 | 80 | 0.2834 | 0.4423 | 0.8845 | 0.8845 | nan | 0.8845 | 0.0 | 0.8845 |
62
+ | 0.174 | 0.25 | 100 | 0.3268 | 0.4612 | 0.9225 | 0.9225 | nan | 0.9225 | 0.0 | 0.9225 |
63
+ | 0.1597 | 0.3 | 120 | 0.2900 | 0.4229 | 0.8457 | 0.8457 | nan | 0.8457 | 0.0 | 0.8457 |
64
+ | 0.2165 | 0.35 | 140 | 0.2723 | 0.4411 | 0.8822 | 0.8822 | nan | 0.8822 | 0.0 | 0.8822 |
65
+ | 0.2 | 0.4 | 160 | 0.2598 | 0.4167 | 0.8334 | 0.8334 | nan | 0.8334 | 0.0 | 0.8334 |
66
+ | 0.577 | 0.45 | 180 | 0.3185 | 0.4708 | 0.9416 | 0.9416 | nan | 0.9416 | 0.0 | 0.9416 |
67
+ | 0.2466 | 0.5 | 200 | 0.2305 | 0.4295 | 0.8589 | 0.8589 | nan | 0.8589 | 0.0 | 0.8589 |
68
+ | 0.1742 | 0.55 | 220 | 0.2439 | 0.4544 | 0.9089 | 0.9089 | nan | 0.9089 | 0.0 | 0.9089 |
69
+ | 0.1764 | 0.6 | 240 | 0.2318 | 0.4359 | 0.8719 | 0.8719 | nan | 0.8719 | 0.0 | 0.8719 |
70
+ | 0.1432 | 0.65 | 260 | 0.2253 | 0.4318 | 0.8636 | 0.8636 | nan | 0.8636 | 0.0 | 0.8636 |
71
+ | 0.1472 | 0.7 | 280 | 0.2193 | 0.4353 | 0.8707 | 0.8707 | nan | 0.8707 | 0.0 | 0.8707 |
72
+ | 0.4737 | 0.75 | 300 | 0.2347 | 0.4407 | 0.8813 | 0.8813 | nan | 0.8813 | 0.0 | 0.8813 |
73
+ | 0.1567 | 0.8 | 320 | 0.2212 | 0.4248 | 0.8496 | 0.8496 | nan | 0.8496 | 0.0 | 0.8496 |
74
+ | 0.0832 | 0.85 | 340 | 0.2170 | 0.4426 | 0.8852 | 0.8852 | nan | 0.8852 | 0.0 | 0.8852 |
75
+ | 0.1718 | 0.9 | 360 | 0.2079 | 0.4390 | 0.8780 | 0.8780 | nan | 0.8780 | 0.0 | 0.8780 |
76
+ | 0.3256 | 0.95 | 380 | 0.2127 | 0.4576 | 0.9151 | 0.9151 | nan | 0.9151 | 0.0 | 0.9151 |
77
+ | 0.089 | 1.0 | 400 | 0.2249 | 0.4603 | 0.9207 | 0.9207 | nan | 0.9207 | 0.0 | 0.9207 |
78
+ | 0.103 | 1.05 | 420 | 0.2051 | 0.4360 | 0.8720 | 0.8720 | nan | 0.8720 | 0.0 | 0.8720 |
79
+ | 0.3474 | 1.1 | 440 | 0.2216 | 0.4333 | 0.8666 | 0.8666 | nan | 0.8666 | 0.0 | 0.8666 |
80
+ | 0.0851 | 1.15 | 460 | 0.2306 | 0.4681 | 0.9361 | 0.9361 | nan | 0.9361 | 0.0 | 0.9361 |
81
+ | 0.1989 | 1.2 | 480 | 0.2029 | 0.4516 | 0.9032 | 0.9032 | nan | 0.9032 | 0.0 | 0.9032 |
82
+ | 0.2072 | 1.25 | 500 | 0.2076 | 0.4666 | 0.9331 | 0.9331 | nan | 0.9331 | 0.0 | 0.9331 |
83
+ | 0.2898 | 1.3 | 520 | 0.2164 | 0.4645 | 0.9291 | 0.9291 | nan | 0.9291 | 0.0 | 0.9291 |
84
+ | 0.1578 | 1.35 | 540 | 0.2057 | 0.4457 | 0.8914 | 0.8914 | nan | 0.8914 | 0.0 | 0.8914 |
85
+ | 0.2697 | 1.4 | 560 | 0.1973 | 0.4646 | 0.9292 | 0.9292 | nan | 0.9292 | 0.0 | 0.9292 |
86
+ | 0.1269 | 1.45 | 580 | 0.1830 | 0.4467 | 0.8934 | 0.8934 | nan | 0.8934 | 0.0 | 0.8934 |
87
+ | 0.0908 | 1.5 | 600 | 0.1866 | 0.4471 | 0.8941 | 0.8941 | nan | 0.8941 | 0.0 | 0.8941 |
88
+ | 0.0614 | 1.55 | 620 | 0.1983 | 0.4632 | 0.9264 | 0.9264 | nan | 0.9264 | 0.0 | 0.9264 |
89
+ | 0.1043 | 1.6 | 640 | 0.1941 | 0.4598 | 0.9196 | 0.9196 | nan | 0.9196 | 0.0 | 0.9196 |
90
+ | 0.0532 | 1.65 | 660 | 0.1920 | 0.4553 | 0.9106 | 0.9106 | nan | 0.9106 | 0.0 | 0.9106 |
91
+ | 0.5912 | 1.7 | 680 | 0.1880 | 0.4530 | 0.9059 | 0.9059 | nan | 0.9059 | 0.0 | 0.9059 |
92
+ | 0.0604 | 1.75 | 700 | 0.1964 | 0.4611 | 0.9221 | 0.9221 | nan | 0.9221 | 0.0 | 0.9221 |
93
+ | 0.0899 | 1.8 | 720 | 0.1975 | 0.4623 | 0.9245 | 0.9245 | nan | 0.9245 | 0.0 | 0.9245 |
94
+ | 0.1153 | 1.85 | 740 | 0.1866 | 0.4580 | 0.9160 | 0.9160 | nan | 0.9160 | 0.0 | 0.9160 |
95
+ | 0.1038 | 1.9 | 760 | 0.1998 | 0.4652 | 0.9304 | 0.9304 | nan | 0.9304 | 0.0 | 0.9304 |
96
+ | 0.1448 | 1.95 | 780 | 0.1977 | 0.4624 | 0.9248 | 0.9248 | nan | 0.9248 | 0.0 | 0.9248 |
97
+ | 0.1298 | 2.0 | 800 | 0.1893 | 0.4552 | 0.9104 | 0.9104 | nan | 0.9104 | 0.0 | 0.9104 |
98
 
99
 
100
  ### Framework versions
config.json CHANGED
@@ -28,80 +28,14 @@
28
  256
29
  ],
30
  "id2label": {
31
- "0": "unlabeled",
32
- "1": "flat-road",
33
- "2": "flat-sidewalk",
34
- "3": "flat-crosswalk",
35
- "4": "flat-cyclinglane",
36
- "5": "flat-parkingdriveway",
37
- "6": "flat-railtrack",
38
- "7": "flat-curb",
39
- "8": "human-person",
40
- "9": "human-rider",
41
- "10": "vehicle-car",
42
- "11": "vehicle-truck",
43
- "12": "vehicle-bus",
44
- "13": "vehicle-tramtrain",
45
- "14": "vehicle-motorcycle",
46
- "15": "vehicle-bicycle",
47
- "16": "vehicle-caravan",
48
- "17": "vehicle-cartrailer",
49
- "18": "construction-building",
50
- "19": "construction-door",
51
- "20": "construction-wall",
52
- "21": "construction-fenceguardrail",
53
- "22": "construction-bridge",
54
- "23": "construction-tunnel",
55
- "24": "construction-stairs",
56
- "25": "object-pole",
57
- "26": "object-trafficsign",
58
- "27": "object-trafficlight",
59
- "28": "nature-vegetation",
60
- "29": "nature-terrain",
61
- "30": "sky",
62
- "31": "void-ground",
63
- "32": "void-dynamic",
64
- "33": "void-static",
65
- "34": "void-unclear"
66
  },
67
  "image_size": 224,
68
  "initializer_range": 0.02,
69
  "label2id": {
70
- "construction-bridge": 22,
71
- "construction-building": 18,
72
- "construction-door": 19,
73
- "construction-fenceguardrail": 21,
74
- "construction-stairs": 24,
75
- "construction-tunnel": 23,
76
- "construction-wall": 20,
77
- "flat-crosswalk": 3,
78
- "flat-curb": 7,
79
- "flat-cyclinglane": 4,
80
- "flat-parkingdriveway": 5,
81
- "flat-railtrack": 6,
82
- "flat-road": 1,
83
- "flat-sidewalk": 2,
84
- "human-person": 8,
85
- "human-rider": 9,
86
- "nature-terrain": 29,
87
- "nature-vegetation": 28,
88
- "object-pole": 25,
89
- "object-trafficlight": 27,
90
- "object-trafficsign": 26,
91
- "sky": 30,
92
- "unlabeled": 0,
93
- "vehicle-bicycle": 15,
94
- "vehicle-bus": 12,
95
- "vehicle-car": 10,
96
- "vehicle-caravan": 16,
97
- "vehicle-cartrailer": 17,
98
- "vehicle-motorcycle": 14,
99
- "vehicle-tramtrain": 13,
100
- "vehicle-truck": 11,
101
- "void-dynamic": 32,
102
- "void-ground": 31,
103
- "void-static": 33,
104
- "void-unclear": 34
105
  },
106
  "layer_norm_eps": 1e-06,
107
  "mlp_ratios": [
 
28
  256
29
  ],
30
  "id2label": {
31
+ "0": "other",
32
+ "1": "flat-sidewalk"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
  },
34
  "image_size": 224,
35
  "initializer_range": 0.02,
36
  "label2id": {
37
+ "flat-sidewalk": 1,
38
+ "other": 0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39
  },
40
  "layer_norm_eps": 1e-06,
41
  "mlp_ratios": [
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