--- library_name: transformers base_model: segformer-b0-finetuned-ade-512-512 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 [segformer-b0-finetuned-ade-512-512](https://huggingface.co/segformer-b0-finetuned-ade-512-512) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 2.4704 - Mean Iou: 0.1228 - Mean Accuracy: 0.1842 - Overall Accuracy: 0.6771 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8619 - Accuracy Flat-sidewalk: 0.8589 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.0530 - Accuracy Flat-parkingdriveway: 0.0006 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.8886 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: nan - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.6177 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9365 - Accuracy Nature-terrain: 0.5350 - Accuracy Sky: 0.9510 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0074 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.4368 - Iou Flat-sidewalk: 0.7208 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.0505 - Iou Flat-parkingdriveway: 0.0005 - Iou Flat-railtrack: 0.0 - Iou Flat-curb: 0.0 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.5175 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: nan - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5053 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.6868 - Iou Nature-terrain: 0.3948 - Iou Sky: 0.6104 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0073 - 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - 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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| 3.0949 | 0.2 | 20 | 3.0458 | 0.0819 | 0.1460 | 0.5398 | nan | 0.7787 | 0.7924 | 0.0044 | 0.1968 | 0.0005 | nan | 0.0002 | 0.0438 | 0.0096 | 0.8797 | 0.0059 | 0.0 | nan | 0.0130 | 0.0 | 0.0 | 0.0 | 0.0409 | 0.0 | 0.0023 | 0.0 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.2713 | 0.7608 | 0.0000 | 0.0154 | 0.0043 | 0.0720 | 0.0 | 0.4417 | 0.6871 | 0.0038 | 0.1179 | 0.0005 | 0.0 | 0.0002 | 0.0246 | 0.0004 | 0.3778 | 0.0036 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0401 | 0.0 | 0.0023 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5177 | 0.2196 | 0.4219 | 0.0000 | 0.0011 | 0.0042 | 0.0001 | | 2.9043 | 0.4 | 40 | 2.7801 | 0.1036 | 0.1809 | 0.6489 | nan | 0.8303 | 0.8567 | 0.0000 | 0.1591 | 0.0005 | nan | 0.0001 | 0.0036 | 0.0 | 0.9453 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.3779 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8374 | 0.6238 | 0.9717 | 0.0 | 0.0000 | 0.0014 | 0.0 | 0.0 | 0.4619 | 0.7132 | 0.0000 | 0.1384 | 0.0005 | 0.0 | 0.0001 | 0.0033 | 0.0 | 0.3443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3474 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6680 | 0.4432 | 0.5029 | 0.0 | 0.0000 | 0.0014 | 0.0 | | 2.7418 | 0.6 | 60 | 2.5939 | 0.1131 | 0.1800 | 0.6631 | nan | 0.8577 | 0.8508 | 0.0 | 0.0594 | 0.0004 | nan | 0.0000 | 0.0002 | 0.0 | 0.9340 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5521 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9077 | 0.4507 | 0.9612 | 0.0 | 0.0 | 0.0044 | 0.0 | nan | 0.4298 | 0.7138 | 0.0 | 0.0555 | 0.0004 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.4210 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4768 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6815 | 0.3562 | 0.5935 | 0.0 | 0.0 | 0.0043 | 0.0 | | 2.6386 | 0.8 | 80 | 2.4834 | 0.1216 | 0.1831 | 0.6776 | nan | 0.8479 | 0.8715 | 0.0 | 0.0632 | 0.0005 | nan | 0.0 | 0.0000 | 0.0 | 0.8980 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6096 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9324 | 0.4880 | 0.9573 | 0.0 | 0.0 | 0.0088 | 0.0 | nan | 0.4463 | 0.7197 | 0.0 | 0.0604 | 0.0005 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.5022 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5004 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6846 | 0.3790 | 0.5905 | 0.0 | 0.0 | 0.0086 | 0.0 | | 2.4504 | 1.0 | 100 | 2.4704 | 0.1228 | 0.1842 | 0.6771 | nan | 0.8619 | 0.8589 | 0.0 | 0.0530 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8886 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6177 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9365 | 0.5350 | 0.9510 | 0.0 | 0.0 | 0.0074 | 0.0 | nan | 0.4368 | 0.7208 | 0.0 | 0.0505 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5175 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5053 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6868 | 0.3948 | 0.6104 | 0.0 | 0.0 | 0.0073 | 0.0 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.1.1+cu118 - Datasets 3.2.0 - Tokenizers 0.21.0