--- license: other tags: - generated_from_trainer - image_segmentation model-index: - name: segformer-b0-finetuned-segments-sidewalk results: [] datasets: - segments/sidewalk-semantic library_name: transformers pipeline_tag: image-segmentation --- # segformer-b0-finetuned-segments-sidewalk This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5449 - Mean Iou: 0.3292 - Mean Accuracy: 0.3907 - Overall Accuracy: 0.8555 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8585 - Accuracy Flat-sidewalk: 0.9611 - Accuracy Flat-crosswalk: 0.7673 - Accuracy Flat-cyclinglane: 0.8223 - Accuracy Flat-parkingdriveway: 0.5127 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.4937 - Accuracy Human-person: 0.7164 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9332 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: nan - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.3858 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9040 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.5848 - Accuracy Construction-fenceguardrail: 0.4417 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.3156 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9413 - Accuracy Nature-terrain: 0.8456 - Accuracy Sky: 0.9600 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.2780 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7447 - Iou Flat-sidewalk: 0.8755 - Iou Flat-crosswalk: 0.6244 - Iou Flat-cyclinglane: 0.7325 - Iou Flat-parkingdriveway: 0.3997 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.3974 - Iou Human-person: 0.4985 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.7798 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: nan - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.2904 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.7233 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.4555 - Iou Construction-fenceguardrail: 0.3734 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.2484 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8451 - Iou Nature-terrain: 0.7346 - Iou Sky: 0.9161 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.2359 - 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### 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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| 1.4172 | 1.87 | 200 | 1.2183 | 0.1696 | 0.2214 | 0.7509 | nan | 0.8882 | 0.9199 | 0.0 | 0.4200 | 0.0164 | nan | 0.0 | 0.0 | 0.0 | 0.8778 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8448 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9430 | 0.8044 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5435 | 0.8135 | 0.0 | 0.3743 | 0.0160 | nan | 0.0 | 0.0 | 0.0 | 0.6044 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5373 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7516 | 0.6550 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1152 | 3.74 | 400 | 0.8946 | 0.1947 | 0.2441 | 0.7852 | nan | 0.8535 | 0.9471 | 0.0 | 0.7379 | 0.2453 | nan | 0.0398 | 0.0 | 0.0 | 0.8882 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8746 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.9526 | 0.8285 | 0.9448 | 0.0 | 0.0 | 0.0019 | 0.0 | nan | 0.6355 | 0.8321 | 0.0 | 0.5529 | 0.1940 | nan | 0.0392 | 0.0 | 0.0 | 0.6807 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5913 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.7701 | 0.6777 | 0.8567 | 0.0 | 0.0 | 0.0019 | 0.0 | | 0.6637 | 5.61 | 600 | 0.7447 | 0.2349 | 0.2841 | 0.8104 | nan | 0.8589 | 0.9451 | 0.4455 | 0.8008 | 0.3753 | nan | 0.3267 | 0.0380 | 0.0 | 0.8920 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9227 | 0.0 | 0.0938 | 0.0 | 0.0 | nan | 0.0 | 0.0167 | 0.0 | 0.0 | 0.9291 | 0.8677 | 0.9557 | 0.0 | 0.0 | 0.0562 | 0.0 | nan | 0.6768 | 0.8543 | 0.4064 | 0.6414 | 0.2914 | nan | 0.2749 | 0.0376 | 0.0 | 0.7268 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6078 | 0.0 | 0.0879 | 0.0 | 0.0 | nan | 0.0 | 0.0164 | 0.0 | 0.0 | 0.8005 | 0.6817 | 0.8918 | 0.0 | 0.0 | 0.0525 | 0.0 | | 0.673 | 7.48 | 800 | 0.6631 | 0.2691 | 0.3202 | 0.8278 | nan | 0.8387 | 0.9575 | 0.6176 | 0.7938 | 0.4208 | nan | 0.3575 | 0.3977 | 0.0 | 0.9264 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.0 | 0.4035 | 0.0 | 0.0 | nan | 0.0 | 0.1137 | 0.0 | 0.0 | 0.9495 | 0.8165 | 0.9453 | 0.0 | 0.0 | 0.1599 | 0.0 | nan | 0.7042 | 0.8567 | 0.5239 | 0.6600 | 0.3246 | nan | 0.3003 | 0.3212 | 0.0 | 0.7246 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6749 | 0.0 | 0.3113 | 0.0 | 0.0 | nan | 0.0 | 0.1038 | 0.0 | 0.0 | 0.8147 | 0.7070 | 0.9008 | 0.0 | 0.0 | 0.1445 | 0.0 | | 0.502 | 9.35 | 1000 | 0.6249 | 0.2818 | 0.3371 | 0.8345 | nan | 0.8332 | 0.9538 | 0.7158 | 0.8344 | 0.4079 | nan | 0.4420 | 0.4941 | 0.0 | 0.9275 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.9102 | 0.0 | 0.4787 | 0.0253 | 0.0 | nan | 0.0 | 0.1454 | 0.0 | 0.0 | 0.9460 | 0.8350 | 0.9588 | 0.0 | 0.0 | 0.1887 | 0.0 | nan | 0.7176 | 0.8635 | 0.6035 | 0.6519 | 0.3246 | nan | 0.3545 | 0.3720 | 0.0 | 0.7524 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.3286 | 0.0250 | 0.0 | nan | 0.0 | 0.1309 | 0.0 | 0.0 | 0.8335 | 0.7300 | 0.9037 | 0.0 | 0.0 | 0.1584 | 0.0 | | 0.9687 | 11.21 | 1200 | 0.5786 | 0.3093 | 0.3675 | 0.8471 | nan | 0.8703 | 0.9504 | 0.7382 | 0.7705 | 0.5297 | nan | 0.4804 | 0.6250 | 0.0 | 0.9168 | 0.0 | nan | nan | 0.0 | 0.1397 | 0.0 | 0.0 | 0.9228 | 0.0 | 0.5710 | 0.3183 | 0.0 | nan | 0.0 | 0.2252 | 0.0 | 0.0 | 0.9314 | 0.8840 | 0.9536 | 0.0 | 0.0 | 0.1981 | 0.0 | nan | 0.7380 | 0.8743 | 0.5825 | 0.7093 | 0.3829 | nan | 0.3743 | 0.4600 | 0.0 | 0.7727 | 0.0 | nan | nan | 0.0 | 0.1372 | 0.0 | 0.0 | 0.7008 | 0.0 | 0.4315 | 0.2847 | 0.0 | nan | 0.0 | 0.1930 | 0.0 | 0.0 | 0.8397 | 0.7121 | 0.9109 | 0.0 | 0.0 | 0.1761 | 0.0 | | 0.4681 | 13.08 | 1400 | 0.5759 | 0.3106 | 0.3665 | 0.8462 | nan | 0.8586 | 0.9572 | 0.5158 | 0.8121 | 0.5195 | nan | 0.4539 | 0.6944 | 0.0 | 0.9308 | 0.0 | nan | nan | 0.0 | 0.2759 | 0.0 | 0.0 | 0.9126 | 0.0 | 0.4927 | 0.3145 | 0.0 | nan | 0.0 | 0.2566 | 0.0 | 0.0 | 0.9396 | 0.8736 | 0.9644 | 0.0 | 0.0 | 0.2226 | 0.0 | nan | 0.7134 | 0.8742 | 0.5009 | 0.7146 | 0.4018 | nan | 0.3726 | 0.4661 | 0.0 | 0.7674 | 0.0 | nan | nan | 0.0 | 0.2501 | 0.0 | 0.0 | 0.6997 | 0.0 | 0.3933 | 0.2827 | 0.0 | nan | 0.0 | 0.2137 | 0.0 | 0.0 | 0.8377 | 0.7212 | 0.9109 | 0.0 | 0.0 | 0.1964 | 0.0 | | 0.5374 | 14.95 | 1600 | 0.5534 | 0.3232 | 0.3823 | 0.8518 | nan | 0.8607 | 0.9545 | 0.7138 | 0.8398 | 0.5129 | nan | 0.4823 | 0.7055 | 0.0 | 0.9225 | 0.0 | nan | nan | 0.0 | 0.3058 | 0.0 | 0.0 | 0.8999 | 0.0 | 0.5436 | 0.3798 | 0.0 | nan | 0.0 | 0.2878 | 0.0 | 0.0 | 0.9485 | 0.8388 | 0.9598 | 0.0 | 0.0 | 0.3145 | 0.0 | nan | 0.7336 | 0.8788 | 0.6094 | 0.7062 | 0.3966 | nan | 0.3854 | 0.4897 | 0.0 | 0.7823 | 0.0 | nan | nan | 0.0 | 0.2782 | 0.0 | 0.0 | 0.7148 | 0.0 | 0.4182 | 0.3304 | 0.0 | nan | 0.0 | 0.2324 | 0.0 | 0.0 | 0.8415 | 0.7356 | 0.9130 | 0.0 | 0.0 | 0.2491 | 0.0 | | 0.6115 | 16.82 | 1800 | 0.5528 | 0.3266 | 0.3849 | 0.8539 | nan | 0.8521 | 0.9611 | 0.6840 | 0.8291 | 0.5057 | nan | 0.5070 | 0.7165 | 0.0 | 0.9267 | 0.0 | nan | nan | 0.0 | 0.3659 | 0.0 | 0.0 | 0.9007 | 0.0 | 0.5844 | 0.3961 | 0.0 | nan | 0.0 | 0.2827 | 0.0 | 0.0 | 0.9517 | 0.8371 | 0.9602 | 0.0 | 0.0 | 0.2848 | 0.0 | nan | 0.7414 | 0.8721 | 0.6312 | 0.7245 | 0.3979 | nan | 0.3987 | 0.4932 | 0.0 | 0.7799 | 0.0 | nan | nan | 0.0 | 0.2788 | 0.0 | 0.0 | 0.7242 | 0.0 | 0.4542 | 0.3464 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8384 | 0.7318 | 0.9141 | 0.0 | 0.0 | 0.2386 | 0.0 | | 0.4766 | 18.69 | 2000 | 0.5449 | 0.3292 | 0.3907 | 0.8555 | nan | 0.8585 | 0.9611 | 0.7673 | 0.8223 | 0.5127 | nan | 0.4937 | 0.7164 | 0.0 | 0.9332 | 0.0 | nan | nan | 0.0 | 0.3858 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.5848 | 0.4417 | 0.0 | nan | 0.0 | 0.3156 | 0.0 | 0.0 | 0.9413 | 0.8456 | 0.9600 | 0.0 | 0.0 | 0.2780 | 0.0 | nan | 0.7447 | 0.8755 | 0.6244 | 0.7325 | 0.3997 | nan | 0.3974 | 0.4985 | 0.0 | 0.7798 | 0.0 | nan | nan | 0.0 | 0.2904 | 0.0 | 0.0 | 0.7233 | 0.0 | 0.4555 | 0.3734 | 0.0 | nan | 0.0 | 0.2484 | 0.0 | 0.0 | 0.8451 | 0.7346 | 0.9161 | 0.0 | 0.0 | 0.2359 | 0.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3