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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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- generated_from_trainer |
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model-index: |
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- name: SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001 |
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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_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001 |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0217 |
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- Mean Iou: 0.9708 |
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- Mean Accuracy: 0.9835 |
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- Overall Accuracy: 0.9941 |
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- Accuracy Background: 0.9965 |
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- Accuracy Melt: 0.9584 |
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- Accuracy Substrate: 0.9957 |
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- Iou Background: 0.9940 |
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- Iou Melt: 0.9288 |
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- Iou Substrate: 0.9895 |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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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 Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:| |
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| 0.1107 | 0.8850 | 50 | 0.1152 | 0.8138 | 0.8439 | 0.9627 | 0.9781 | 0.5623 | 0.9914 | 0.9677 | 0.5412 | 0.9325 | |
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| 0.0564 | 1.7699 | 100 | 0.0520 | 0.9163 | 0.9432 | 0.9829 | 0.9967 | 0.8488 | 0.9841 | 0.9806 | 0.7963 | 0.9721 | |
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| 0.0296 | 2.6549 | 150 | 0.0270 | 0.9557 | 0.9821 | 0.9906 | 0.9916 | 0.9621 | 0.9928 | 0.9893 | 0.8939 | 0.9839 | |
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| 0.042 | 3.5398 | 200 | 0.0226 | 0.9619 | 0.9763 | 0.9922 | 0.9934 | 0.9384 | 0.9969 | 0.9917 | 0.9077 | 0.9862 | |
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| 0.0166 | 4.4248 | 250 | 0.0300 | 0.9616 | 0.9768 | 0.9904 | 0.9957 | 0.9446 | 0.9903 | 0.9872 | 0.9153 | 0.9823 | |
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| 0.0159 | 5.3097 | 300 | 0.0203 | 0.9658 | 0.9863 | 0.9931 | 0.9946 | 0.9701 | 0.9941 | 0.9923 | 0.9169 | 0.9883 | |
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| 0.0121 | 6.1947 | 350 | 0.0221 | 0.9645 | 0.9795 | 0.9928 | 0.9937 | 0.9480 | 0.9968 | 0.9923 | 0.9141 | 0.9872 | |
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| 0.0149 | 7.0796 | 400 | 0.0220 | 0.9648 | 0.9821 | 0.9930 | 0.9949 | 0.9565 | 0.9951 | 0.9930 | 0.9138 | 0.9874 | |
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| 0.0352 | 7.9646 | 450 | 0.0215 | 0.9658 | 0.9764 | 0.9933 | 0.9959 | 0.9361 | 0.9971 | 0.9935 | 0.9158 | 0.9880 | |
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| 0.0106 | 8.8496 | 500 | 0.0201 | 0.9696 | 0.9820 | 0.9939 | 0.9961 | 0.9535 | 0.9962 | 0.9938 | 0.9256 | 0.9892 | |
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| 0.0095 | 9.7345 | 550 | 0.0216 | 0.9674 | 0.9796 | 0.9936 | 0.9955 | 0.9463 | 0.9969 | 0.9936 | 0.9202 | 0.9886 | |
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| 0.009 | 10.6195 | 600 | 0.0209 | 0.9702 | 0.9821 | 0.9941 | 0.9966 | 0.9539 | 0.9960 | 0.9940 | 0.9273 | 0.9894 | |
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| 0.0106 | 11.5044 | 650 | 0.0211 | 0.9700 | 0.9830 | 0.9940 | 0.9964 | 0.9568 | 0.9958 | 0.9940 | 0.9266 | 0.9893 | |
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| 0.0099 | 12.3894 | 700 | 0.0217 | 0.9708 | 0.9835 | 0.9941 | 0.9965 | 0.9584 | 0.9957 | 0.9940 | 0.9288 | 0.9895 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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