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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-2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-b0-finetuned-segments-sidewalk-2 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the pixel_values, the label and the {'pixel_values': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x7FCAFB662B60>, 'label': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1x1 at 0x7FCAFB662B30>} datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5116 |
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- Mean Iou: 0.0268 |
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- Mean Accuracy: 0.0661 |
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- Overall Accuracy: 0.2418 |
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- Accuracy Unlabeled: nan |
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- Accuracy Flat-road: 0.0351 |
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- Accuracy Flat-sidewalk: 0.5938 |
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- Accuracy Flat-crosswalk: 0.3236 |
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- Accuracy Flat-cyclinglane: 0.0338 |
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- Accuracy Flat-parkingdriveway: 0.0555 |
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- Accuracy Flat-railtrack: nan |
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- Accuracy Flat-curb: 0.0006 |
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- Accuracy Human-person: 0.0 |
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- Accuracy Human-rider: 0.0003 |
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- Accuracy Vehicle-car: 0.3388 |
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- Accuracy Vehicle-truck: 0.0016 |
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- Accuracy Vehicle-bus: 0.0 |
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- Accuracy Vehicle-tramtrain: 0.2141 |
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- Accuracy Vehicle-motorcycle: 0.0053 |
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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: 0.0888 |
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- Accuracy Construction-building: 0.0391 |
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- Accuracy Construction-door: 0.0 |
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- Accuracy Construction-wall: 0.0074 |
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- Accuracy Construction-fenceguardrail: 0.0239 |
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- Accuracy Construction-bridge: 0.0 |
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- Accuracy Construction-tunnel: nan |
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- Accuracy Construction-stairs: 0.0006 |
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- Accuracy Object-pole: 0.0593 |
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- Accuracy Object-trafficsign: 0.0 |
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- Accuracy Object-trafficlight: 0.0665 |
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- Accuracy Nature-vegetation: 0.0846 |
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- Accuracy Nature-terrain: 0.0002 |
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- Accuracy Sky: 0.0030 |
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- Accuracy Void-ground: 0.0635 |
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- Accuracy Void-dynamic: 0.0004 |
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- Accuracy Void-static: 0.0720 |
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- Accuracy Void-unclear: 0.0022 |
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- Iou Unlabeled: 0.0 |
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- Iou Flat-road: 0.0297 |
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- Iou Flat-sidewalk: 0.4826 |
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- Iou Flat-crosswalk: 0.0624 |
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- Iou Flat-cyclinglane: 0.0279 |
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- Iou Flat-parkingdriveway: 0.0203 |
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- Iou Flat-railtrack: 0.0 |
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- Iou Flat-curb: 0.0005 |
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- Iou Human-person: 0.0 |
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- Iou Human-rider: 0.0001 |
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- Iou Vehicle-car: 0.1389 |
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- Iou Vehicle-truck: 0.0000 |
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- Iou Vehicle-bus: 0.0 |
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- Iou Vehicle-tramtrain: 0.0013 |
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- Iou Vehicle-motorcycle: 0.0007 |
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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: 0.0004 |
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- Iou Construction-building: 0.0383 |
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- Iou Construction-door: 0.0 |
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- Iou Construction-wall: 0.0057 |
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- Iou Construction-fenceguardrail: 0.0127 |
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- Iou Construction-bridge: 0.0 |
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- Iou Construction-tunnel: 0.0 |
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- Iou Construction-stairs: 0.0001 |
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- Iou Object-pole: 0.0085 |
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- Iou Object-trafficsign: 0.0 |
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- Iou Object-trafficlight: 0.0002 |
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- Iou Nature-vegetation: 0.0818 |
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- Iou Nature-terrain: 0.0002 |
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- Iou Sky: 0.0027 |
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- Iou Void-ground: 0.0115 |
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- Iou Void-dynamic: 0.0001 |
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- Iou Void-static: 0.0102 |
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- Iou Void-unclear: 0.0021 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 2 |
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- eval_batch_size: 2 |
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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 |
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- num_epochs: 0.025 |
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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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| 3.5028 | 0.01 | 5 | 3.5307 | 0.0194 | 0.0486 | 0.1779 | nan | 0.0150 | 0.4721 | 0.2351 | 0.0249 | 0.0409 | nan | 0.0003 | 0.0 | 0.0003 | 0.1461 | 0.0231 | 0.0 | 0.2163 | 0.0047 | 0.0 | 0.0 | 0.0318 | 0.0223 | 0.0003 | 0.0136 | 0.0166 | 0.0 | nan | 0.0008 | 0.0511 | 0.0 | 0.0665 | 0.0261 | 0.0005 | 0.0010 | 0.0697 | 0.0014 | 0.0720 | 0.0020 | 0.0 | 0.0128 | 0.3979 | 0.0509 | 0.0221 | 0.0166 | 0.0 | 0.0003 | 0.0 | 0.0001 | 0.0769 | 0.0000 | 0.0 | 0.0015 | 0.0003 | 0.0 | 0.0 | 0.0001 | 0.0219 | 0.0001 | 0.0089 | 0.0103 | 0.0 | 0.0 | 0.0001 | 0.0070 | 0.0 | 0.0001 | 0.0257 | 0.0005 | 0.0009 | 0.0109 | 0.0004 | 0.0099 | 0.0019 | |
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| 3.3613 | 0.03 | 10 | 3.5116 | 0.0268 | 0.0661 | 0.2418 | nan | 0.0351 | 0.5938 | 0.3236 | 0.0338 | 0.0555 | nan | 0.0006 | 0.0 | 0.0003 | 0.3388 | 0.0016 | 0.0 | 0.2141 | 0.0053 | 0.0 | 0.0 | 0.0888 | 0.0391 | 0.0 | 0.0074 | 0.0239 | 0.0 | nan | 0.0006 | 0.0593 | 0.0 | 0.0665 | 0.0846 | 0.0002 | 0.0030 | 0.0635 | 0.0004 | 0.0720 | 0.0022 | 0.0 | 0.0297 | 0.4826 | 0.0624 | 0.0279 | 0.0203 | 0.0 | 0.0005 | 0.0 | 0.0001 | 0.1389 | 0.0000 | 0.0 | 0.0013 | 0.0007 | 0.0 | 0.0 | 0.0004 | 0.0383 | 0.0 | 0.0057 | 0.0127 | 0.0 | 0.0 | 0.0001 | 0.0085 | 0.0 | 0.0002 | 0.0818 | 0.0002 | 0.0027 | 0.0115 | 0.0001 | 0.0102 | 0.0021 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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