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test-carbonate-segmentation2

Image Segmentation
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segformer
vision
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update model card README.md

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+ ---
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+ license: other
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: test-carbonate-segmentation2
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+ results: []
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+ ---
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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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+
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+ # test-carbonate-segmentation2
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3642
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+ - Mean Iou: 0.2180
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+ - Mean Accuracy: 0.3344
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+ - Overall Accuracy: 0.6454
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+ - Accuracy Micrite: nan
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+ - Accuracy Cement: nan
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+ - Accuracy Peloid/pellet/ooid: nan
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+ - Accuracy Biotic: 0.6660
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+ - Accuracy Scale bar: 0.0028
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+ - Iou Micrite: 0.0
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+ - Iou Cement: nan
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+ - Iou Peloid/pellet/ooid: nan
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+ - Iou Biotic: 0.6511
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+ - Iou Scale bar: 0.0028
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Micrite | Accuracy Cement | Accuracy Peloid/pellet/ooid | Accuracy Biotic | Accuracy Scale bar | Iou Micrite | Iou Cement | Iou Peloid/pellet/ooid | Iou Biotic | Iou Scale bar |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:---------------:|:---------------------------:|:---------------:|:------------------:|:-----------:|:----------:|:----------------------:|:----------:|:-------------:|
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+ | 1.2003 | 2.22 | 20 | 1.5260 | 0.4834 | 0.7334 | 0.9835 | nan | nan | nan | 1.0 | 0.4669 | 0.0 | nan | nan | 0.9834 | 0.4669 |
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+ | 1.2006 | 4.44 | 40 | 0.8923 | 0.6346 | 0.9521 | 0.9498 | nan | nan | nan | 0.9497 | 0.9545 | 0.0 | nan | nan | 0.9494 | 0.9545 |
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+ | 1.4233 | 6.67 | 60 | 1.0240 | 0.4417 | 0.6716 | 0.9793 | nan | nan | nan | 0.9995 | 0.3438 | 0.0 | nan | nan | 0.9814 | 0.3438 |
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+ | 1.1735 | 8.89 | 80 | 0.7964 | 0.5230 | 0.7890 | 0.9437 | nan | nan | nan | 0.9539 | 0.6241 | 0.0 | nan | nan | 0.9449 | 0.6241 |
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+ | 1.0242 | 11.11 | 100 | 0.8747 | 0.5322 | 0.8038 | 0.9849 | nan | nan | nan | 0.9969 | 0.6108 | 0.0 | nan | nan | 0.9859 | 0.6108 |
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+ | 0.9161 | 13.33 | 120 | 0.9217 | 0.5133 | 0.7767 | 0.9831 | nan | nan | nan | 0.9967 | 0.5568 | 0.0 | nan | nan | 0.9832 | 0.5568 |
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+ | 0.8102 | 15.56 | 140 | 0.7069 | 0.5923 | 0.8907 | 0.9490 | nan | nan | nan | 0.9529 | 0.8286 | 0.0 | nan | nan | 0.9484 | 0.8286 |
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+ | 0.5436 | 17.78 | 160 | 0.5149 | 0.3206 | 0.4929 | 0.8806 | nan | nan | nan | 0.9062 | 0.0795 | 0.0 | nan | nan | 0.8823 | 0.0795 |
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+ | 0.8517 | 20.0 | 180 | 0.5646 | 0.3748 | 0.5719 | 0.9200 | nan | nan | nan | 0.9430 | 0.2008 | 0.0 | nan | nan | 0.9236 | 0.2008 |
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+ | 0.4532 | 22.22 | 200 | 0.6128 | 0.3133 | 0.4837 | 0.9188 | nan | nan | nan | 0.9475 | 0.0199 | 0.0 | nan | nan | 0.9201 | 0.0199 |
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+ | 1.3133 | 24.44 | 220 | 0.3006 | 0.2391 | 0.3645 | 0.7064 | nan | nan | nan | 0.7290 | 0.0 | 0.0 | nan | nan | 0.7172 | 0.0 |
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+ | 0.4636 | 26.67 | 240 | 0.3260 | 0.1903 | 0.2901 | 0.5259 | nan | nan | nan | 0.5414 | 0.0388 | 0.0 | nan | nan | 0.5320 | 0.0388 |
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+ | 0.9843 | 28.89 | 260 | 0.3663 | 0.2741 | 0.4182 | 0.6986 | nan | nan | nan | 0.7171 | 0.1193 | 0.0 | nan | nan | 0.7031 | 0.1193 |
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+ | 0.7617 | 31.11 | 280 | 0.3338 | 0.2357 | 0.3627 | 0.7030 | nan | nan | nan | 0.7255 | 0.0 | 0.0 | nan | nan | 0.7072 | 0.0 |
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+ | 1.283 | 33.33 | 300 | 0.3395 | 0.2723 | 0.4176 | 0.7232 | nan | nan | nan | 0.7434 | 0.0919 | 0.0 | nan | nan | 0.7250 | 0.0919 |
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+ | 0.6578 | 35.56 | 320 | 0.3382 | 0.2069 | 0.3170 | 0.6143 | nan | nan | nan | 0.6339 | 0.0 | 0.0 | nan | nan | 0.6207 | 0.0 |
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+ | 0.2129 | 37.78 | 340 | 0.3436 | 0.2288 | 0.3525 | 0.6831 | nan | nan | nan | 0.7049 | 0.0 | 0.0 | nan | nan | 0.6863 | 0.0 |
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+ | 0.7001 | 40.0 | 360 | 0.2998 | 0.2001 | 0.3069 | 0.5771 | nan | nan | nan | 0.5950 | 0.0189 | 0.0 | nan | nan | 0.5813 | 0.0189 |
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+ | 0.3866 | 42.22 | 380 | 0.3162 | 0.1840 | 0.2819 | 0.5464 | nan | nan | nan | 0.5639 | 0.0 | 0.0 | nan | nan | 0.5521 | 0.0 |
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+ | 1.2623 | 44.44 | 400 | 0.3431 | 0.2125 | 0.3254 | 0.6172 | nan | nan | nan | 0.6365 | 0.0142 | 0.0 | nan | nan | 0.6234 | 0.0142 |
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+ | 0.6115 | 46.67 | 420 | 0.2987 | 0.2020 | 0.3095 | 0.5998 | nan | nan | nan | 0.6190 | 0.0 | 0.0 | nan | nan | 0.6060 | 0.0 |
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+ | 0.5802 | 48.89 | 440 | 0.3642 | 0.2180 | 0.3344 | 0.6454 | nan | nan | nan | 0.6660 | 0.0028 | 0.0 | nan | nan | 0.6511 | 0.0028 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0
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+ - Pytorch 2.0.1+cu118
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+ - Tokenizers 0.13.3