--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-2 results: [] --- # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.7084 - Mean Iou: 0.0871 - Mean Accuracy: 0.1451 - Overall Accuracy: 0.6167 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.5180 - Accuracy Flat-sidewalk: 0.9088 - Accuracy Flat-crosswalk: 0.0001 - Accuracy Flat-cyclinglane: 0.0259 - Accuracy Flat-parkingdriveway: 0.0 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0012 - Accuracy Human-person: 0.0017 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9553 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0000 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.4663 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0670 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0064 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9708 - Accuracy Nature-terrain: 0.0024 - Accuracy Sky: 0.5740 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.3925 - Iou Flat-sidewalk: 0.6649 - Iou Flat-crosswalk: 0.0001 - Iou Flat-cyclinglane: 0.0249 - Iou Flat-parkingdriveway: 0.0 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0012 - Iou Human-person: 0.0017 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.2851 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0000 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.3825 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0540 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0048 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.6011 - Iou Nature-terrain: 0.0024 - Iou Sky: 0.5451 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | 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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| 2.9796 | 0.4 | 20 | 3.2289 | 0.0664 | 0.1236 | 0.5591 | nan | 0.2423 | 0.9160 | 0.0000 | 0.0110 | 0.0000 | nan | 0.0006 | 0.0021 | 0.0 | 0.9292 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.2970 | 0.0 | 0.1131 | 0.0 | 0.0 | nan | 0.0 | 0.0502 | 0.0 | 0.0 | 0.9793 | 0.0015 | 0.2752 | 0.0 | 0.0148 | 0.0000 | 0.0 | 0.0 | 0.2077 | 0.6059 | 0.0000 | 0.0107 | 0.0000 | 0.0 | 0.0006 | 0.0020 | 0.0 | 0.2985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.2620 | 0.0 | 0.0654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0249 | 0.0 | 0.0 | 0.5724 | 0.0015 | 0.2711 | 0.0 | 0.0027 | 0.0000 | 0.0 | | 2.6315 | 0.8 | 40 | 2.7084 | 0.0871 | 0.1451 | 0.6167 | nan | 0.5180 | 0.9088 | 0.0001 | 0.0259 | 0.0 | nan | 0.0012 | 0.0017 | 0.0 | 0.9553 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.4663 | 0.0 | 0.0670 | 0.0 | 0.0 | nan | 0.0 | 0.0064 | 0.0 | 0.0 | 0.9708 | 0.0024 | 0.5740 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3925 | 0.6649 | 0.0001 | 0.0249 | 0.0 | 0.0 | 0.0012 | 0.0017 | 0.0 | 0.2851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.3825 | 0.0 | 0.0540 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0048 | 0.0 | 0.0 | 0.6011 | 0.0024 | 0.5451 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.41.1 - Pytorch 1.13.1 - Datasets 2.19.1 - Tokenizers 0.19.1