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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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- image-segmentation |
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- vision |
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- generated_from_trainer |
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model-index: |
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- name: segformer-finetuned-biofilm_MRCNNv1_halfjoin |
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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-finetuned-biofilm_MRCNNv1_halfjoin |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the heroza/biofilm_MRCNNv1_halfjoin dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0208 |
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- Mean Iou: 0.4961 |
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- Mean Accuracy: 0.9923 |
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- Overall Accuracy: 0.9923 |
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- Accuracy Background: 0.9923 |
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- Accuracy Biofilm: nan |
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- Iou Background: 0.9923 |
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- Iou Biofilm: 0.0 |
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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: 1337 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Biofilm | Iou Background | Iou Biofilm | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:| |
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| 0.0713 | 1.0 | 478 | 0.0381 | 0.4953 | 0.9906 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | |
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| 0.044 | 2.0 | 956 | 0.0202 | 0.4975 | 0.9949 | 0.9949 | 0.9949 | nan | 0.9949 | 0.0 | |
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| 0.041 | 3.0 | 1434 | 0.0181 | 0.4972 | 0.9945 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | |
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| 0.0361 | 4.0 | 1912 | 0.0203 | 0.4963 | 0.9926 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | |
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| 0.0357 | 5.0 | 2390 | 0.0163 | 0.4971 | 0.9942 | 0.9942 | 0.9942 | nan | 0.9942 | 0.0 | |
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| 0.0336 | 6.0 | 2868 | 0.0340 | 0.4958 | 0.9915 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | |
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| 0.0295 | 7.0 | 3346 | 0.0126 | 0.4978 | 0.9955 | 0.9955 | 0.9955 | nan | 0.9955 | 0.0 | |
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| 0.0251 | 8.0 | 3824 | 0.0220 | 0.4957 | 0.9915 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | |
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| 0.0265 | 9.0 | 4302 | 0.0182 | 0.4966 | 0.9933 | 0.9933 | 0.9933 | nan | 0.9933 | 0.0 | |
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| 0.0238 | 10.0 | 4780 | 0.0155 | 0.4970 | 0.9940 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | |
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| 0.0258 | 11.0 | 5258 | 0.0181 | 0.4966 | 0.9931 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | |
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| 0.0264 | 12.0 | 5736 | 0.0179 | 0.4969 | 0.9938 | 0.9938 | 0.9938 | nan | 0.9938 | 0.0 | |
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| 0.0265 | 13.0 | 6214 | 0.0222 | 0.4959 | 0.9917 | 0.9917 | 0.9917 | nan | 0.9917 | 0.0 | |
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| 0.0219 | 14.0 | 6692 | 0.0200 | 0.4962 | 0.9925 | 0.9925 | 0.9925 | nan | 0.9925 | 0.0 | |
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| 0.0213 | 15.0 | 7170 | 0.0234 | 0.4958 | 0.9916 | 0.9916 | 0.9916 | nan | 0.9916 | 0.0 | |
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| 0.0192 | 16.0 | 7648 | 0.0199 | 0.4961 | 0.9922 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | |
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| 0.0232 | 17.0 | 8126 | 0.0208 | 0.4961 | 0.9923 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | |
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| 0.0219 | 18.0 | 8604 | 0.0245 | 0.4955 | 0.9909 | 0.9909 | 0.9909 | nan | 0.9909 | 0.0 | |
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| 0.0201 | 19.0 | 9082 | 0.0211 | 0.4961 | 0.9922 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | |
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| 0.0192 | 20.0 | 9560 | 0.0207 | 0.4962 | 0.9923 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | |
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| 0.0175 | 20.92 | 10000 | 0.0208 | 0.4961 | 0.9923 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.15.1 |
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