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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-b0-finetuned-raw_img_ready2train_patches |
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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-raw_img_ready2train_patches |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the raw_img_ready2train_patches dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6829 |
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- Mean Iou: 0.4110 |
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- Mean Accuracy: 0.7629 |
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- Overall Accuracy: 0.7631 |
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- Accuracy Unlabeled: nan |
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- Accuracy Eczema: 0.7673 |
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- Accuracy Background: 0.7585 |
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- Iou Unlabeled: 0.0 |
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- Iou Eczema: 0.6284 |
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- Iou Background: 0.6047 |
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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: 5e-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: 1 |
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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 Eczema | Accuracy Background | Iou Unlabeled | Iou Eczema | Iou Background | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------:|:----------:|:--------------:| |
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| 1.0753 | 0.0312 | 5 | 1.0925 | 0.2358 | 0.4682 | 0.4698 | nan | 0.5042 | 0.4322 | 0.0 | 0.3705 | 0.3367 | |
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| 0.9863 | 0.0625 | 10 | 1.0697 | 0.2994 | 0.6182 | 0.6306 | nan | 0.8979 | 0.3385 | 0.0 | 0.5784 | 0.3198 | |
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| 1.0056 | 0.0938 | 15 | 1.0377 | 0.3303 | 0.6678 | 0.6792 | nan | 0.9236 | 0.4121 | 0.0 | 0.6064 | 0.3844 | |
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| 1.0133 | 0.125 | 20 | 1.0006 | 0.3478 | 0.6869 | 0.6950 | nan | 0.8710 | 0.5027 | 0.0 | 0.6008 | 0.4425 | |
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| 0.9748 | 0.1562 | 25 | 0.9689 | 0.3543 | 0.6947 | 0.7022 | nan | 0.8647 | 0.5246 | 0.0 | 0.6043 | 0.4586 | |
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| 0.9367 | 0.1875 | 30 | 0.9417 | 0.3566 | 0.6950 | 0.6965 | nan | 0.7290 | 0.6610 | 0.0 | 0.5583 | 0.5114 | |
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| 0.8363 | 0.2188 | 35 | 0.9118 | 0.3557 | 0.6940 | 0.6959 | nan | 0.7366 | 0.6514 | 0.0 | 0.5600 | 0.5069 | |
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| 1.1431 | 0.25 | 40 | 0.8830 | 0.3575 | 0.6963 | 0.6989 | nan | 0.7556 | 0.6370 | 0.0 | 0.5686 | 0.5039 | |
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| 0.7312 | 0.2812 | 45 | 0.8592 | 0.3680 | 0.7098 | 0.7133 | nan | 0.7888 | 0.6307 | 0.0 | 0.5907 | 0.5133 | |
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| 0.8135 | 0.3125 | 50 | 0.8268 | 0.3559 | 0.6994 | 0.7083 | nan | 0.8992 | 0.4997 | 0.0 | 0.6173 | 0.4505 | |
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| 0.7528 | 0.3438 | 55 | 0.8110 | 0.3525 | 0.6960 | 0.7053 | nan | 0.9055 | 0.4866 | 0.0 | 0.6162 | 0.4412 | |
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| 0.8405 | 0.375 | 60 | 0.7967 | 0.3518 | 0.6950 | 0.7041 | nan | 0.9008 | 0.4893 | 0.0 | 0.6140 | 0.4415 | |
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| 0.7865 | 0.4062 | 65 | 0.7791 | 0.3561 | 0.6992 | 0.7075 | nan | 0.8869 | 0.5116 | 0.0 | 0.6130 | 0.4553 | |
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| 0.8309 | 0.4375 | 70 | 0.7650 | 0.3652 | 0.7083 | 0.7147 | nan | 0.8512 | 0.5655 | 0.0 | 0.6090 | 0.4864 | |
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| 0.6775 | 0.4688 | 75 | 0.7615 | 0.3613 | 0.7044 | 0.7115 | nan | 0.8651 | 0.5437 | 0.0 | 0.6102 | 0.4738 | |
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| 0.7033 | 0.5 | 80 | 0.7498 | 0.3737 | 0.7179 | 0.7227 | nan | 0.8260 | 0.6099 | 0.0 | 0.6087 | 0.5125 | |
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| 0.8377 | 0.5312 | 85 | 0.7443 | 0.3790 | 0.7243 | 0.7290 | nan | 0.8303 | 0.6184 | 0.0 | 0.6154 | 0.5217 | |
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| 0.825 | 0.5625 | 90 | 0.7547 | 0.3676 | 0.7125 | 0.7201 | nan | 0.8840 | 0.5411 | 0.0 | 0.6225 | 0.4802 | |
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| 0.7408 | 0.5938 | 95 | 0.7415 | 0.3767 | 0.7228 | 0.7295 | nan | 0.8747 | 0.5708 | 0.0 | 0.6281 | 0.5021 | |
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| 0.8087 | 0.625 | 100 | 0.7201 | 0.3926 | 0.7404 | 0.7445 | nan | 0.8318 | 0.6491 | 0.0 | 0.6296 | 0.5483 | |
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| 0.7146 | 0.6562 | 105 | 0.7096 | 0.4002 | 0.7493 | 0.7520 | nan | 0.8109 | 0.6877 | 0.0 | 0.6307 | 0.5699 | |
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| 0.6875 | 0.6875 | 110 | 0.7047 | 0.4010 | 0.7502 | 0.7541 | nan | 0.8398 | 0.6606 | 0.0 | 0.6407 | 0.5621 | |
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| 0.6382 | 0.7188 | 115 | 0.7031 | 0.3982 | 0.7471 | 0.7519 | nan | 0.8543 | 0.6400 | 0.0 | 0.6426 | 0.5521 | |
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| 0.6551 | 0.75 | 120 | 0.6953 | 0.4018 | 0.7512 | 0.7553 | nan | 0.8450 | 0.6573 | 0.0 | 0.6433 | 0.5621 | |
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| 0.7074 | 0.7812 | 125 | 0.6912 | 0.4054 | 0.7553 | 0.7583 | nan | 0.8236 | 0.6871 | 0.0 | 0.6402 | 0.5760 | |
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| 0.768 | 0.8125 | 130 | 0.6866 | 0.4048 | 0.7546 | 0.7579 | nan | 0.8278 | 0.6814 | 0.0 | 0.6410 | 0.5736 | |
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| 0.7543 | 0.8438 | 135 | 0.6851 | 0.4031 | 0.7526 | 0.7564 | nan | 0.8374 | 0.6679 | 0.0 | 0.6422 | 0.5671 | |
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| 0.7107 | 0.875 | 140 | 0.6803 | 0.6122 | 0.7586 | 0.7608 | nan | 0.8071 | 0.7101 | nan | 0.6379 | 0.5865 | |
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| 0.7054 | 0.9062 | 145 | 0.6799 | 0.4098 | 0.7608 | 0.7622 | nan | 0.7924 | 0.7292 | 0.0 | 0.6350 | 0.5943 | |
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| 1.1302 | 0.9375 | 150 | 0.6801 | 0.4103 | 0.7616 | 0.7626 | nan | 0.7840 | 0.7393 | 0.0 | 0.6330 | 0.5981 | |
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| 0.6037 | 0.9688 | 155 | 0.6827 | 0.4111 | 0.7628 | 0.7632 | nan | 0.7721 | 0.7534 | 0.0 | 0.6300 | 0.6032 | |
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| 0.8577 | 1.0 | 160 | 0.6829 | 0.4110 | 0.7629 | 0.7631 | nan | 0.7673 | 0.7585 | 0.0 | 0.6284 | 0.6047 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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