ruisusanofi
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segformer-b0-finetuned-raw_img_ready2train_patches

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

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