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--- |
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license: other |
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base_model: nvidia/mit-b5 |
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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-b5-finetuned-segments-instryde-foot-test |
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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-b5-finetuned-segments-instryde-foot-test |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0149 |
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- Mean Iou: 0.4800 |
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- Mean Accuracy: 0.9599 |
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- Overall Accuracy: 0.9599 |
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- Per Category Iou: [0.0, 0.9599216842864238] |
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- Per Category Accuracy: [nan, 0.9599216842864238] |
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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: 8 |
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- eval_batch_size: 8 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:| |
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| 0.1024 | 0.27 | 20 | 0.2085 | 0.4534 | 0.9067 | 0.9067 | [0.0, 0.9067344993758137] | [nan, 0.9067344993758137] | |
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| 0.0431 | 0.53 | 40 | 0.0487 | 0.4604 | 0.9207 | 0.9207 | [0.0, 0.9207331455341442] | [nan, 0.9207331455341442] | |
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| 0.0354 | 0.8 | 60 | 0.0319 | 0.4577 | 0.9155 | 0.9155 | [0.0, 0.9154662028576415] | [nan, 0.9154662028576415] | |
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| 0.0389 | 1.07 | 80 | 0.0276 | 0.4629 | 0.9257 | 0.9257 | [0.0, 0.9257162800419576] | [nan, 0.9257162800419576] | |
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| 0.0208 | 1.33 | 100 | 0.0244 | 0.4702 | 0.9404 | 0.9404 | [0.0, 0.9403945317069335] | [nan, 0.9403945317069335] | |
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| 0.0241 | 1.6 | 120 | 0.0212 | 0.4703 | 0.9406 | 0.9406 | [0.0, 0.9406131407017349] | [nan, 0.9406131407017349] | |
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| 0.0167 | 1.87 | 140 | 0.0208 | 0.4761 | 0.9521 | 0.9521 | [0.0, 0.9521215619420916] | [nan, 0.9521215619420916] | |
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| 0.0156 | 2.13 | 160 | 0.0205 | 0.4612 | 0.9224 | 0.9224 | [0.0, 0.9224359945462809] | [nan, 0.9224359945462809] | |
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| 0.0156 | 2.4 | 180 | 0.0208 | 0.4734 | 0.9468 | 0.9468 | [0.0, 0.9467575875538612] | [nan, 0.9467575875538612] | |
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| 0.0167 | 2.67 | 200 | 0.0182 | 0.4833 | 0.9667 | 0.9667 | [0.0, 0.9666659635383208] | [nan, 0.9666659635383208] | |
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| 0.0145 | 2.93 | 220 | 0.0243 | 0.4351 | 0.8702 | 0.8702 | [0.0, 0.8702122233110058] | [nan, 0.8702122233110058] | |
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| 0.0114 | 3.2 | 240 | 0.0176 | 0.4686 | 0.9373 | 0.9373 | [0.0, 0.93726765603217] | [nan, 0.93726765603217] | |
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| 0.0155 | 3.47 | 260 | 0.0161 | 0.4770 | 0.9541 | 0.9541 | [0.0, 0.9540767701096305] | [nan, 0.9540767701096305] | |
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| 0.0158 | 3.73 | 280 | 0.0169 | 0.4684 | 0.9368 | 0.9368 | [0.0, 0.9368239181251786] | [nan, 0.9368239181251786] | |
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| 0.0114 | 4.0 | 300 | 0.0162 | 0.4777 | 0.9554 | 0.9554 | [0.0, 0.9554348305492647] | [nan, 0.9554348305492647] | |
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| 0.0112 | 4.27 | 320 | 0.0159 | 0.4839 | 0.9678 | 0.9678 | [0.0, 0.9677532556440432] | [nan, 0.9677532556440432] | |
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| 0.0131 | 4.53 | 340 | 0.0154 | 0.4811 | 0.9622 | 0.9622 | [0.0, 0.9622032718479555] | [nan, 0.9622032718479555] | |
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| 0.0101 | 4.8 | 360 | 0.0156 | 0.4683 | 0.9367 | 0.9367 | [0.0, 0.9366846987126999] | [nan, 0.9366846987126999] | |
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| 0.0102 | 5.07 | 380 | 0.0152 | 0.4758 | 0.9517 | 0.9517 | [0.0, 0.9516509773164403] | [nan, 0.9516509773164403] | |
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| 0.0101 | 5.33 | 400 | 0.0169 | 0.4884 | 0.9768 | 0.9768 | [0.0, 0.9768393358121804] | [nan, 0.9768393358121804] | |
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| 0.0082 | 5.6 | 420 | 0.0150 | 0.4761 | 0.9522 | 0.9522 | [0.0, 0.9522462074215836] | [nan, 0.9522462074215836] | |
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| 0.01 | 5.87 | 440 | 0.0152 | 0.4788 | 0.9576 | 0.9576 | [0.0, 0.9575745140264517] | [nan, 0.9575745140264517] | |
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| 0.0098 | 6.13 | 460 | 0.0148 | 0.4783 | 0.9565 | 0.9565 | [0.0, 0.9565489693736469] | [nan, 0.9565489693736469] | |
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| 0.0088 | 6.4 | 480 | 0.0153 | 0.4795 | 0.9591 | 0.9591 | [0.0, 0.959051850601846] | [nan, 0.959051850601846] | |
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| 0.0091 | 6.67 | 500 | 0.0152 | 0.4828 | 0.9656 | 0.9656 | [0.0, 0.965590177169167] | [nan, 0.965590177169167] | |
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| 0.0102 | 6.93 | 520 | 0.0149 | 0.4800 | 0.9599 | 0.9599 | [0.0, 0.9599216842864238] | [nan, 0.9599216842864238] | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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