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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: segcrack9k_conglomerate_segformer_aug |
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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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# segcrack9k_conglomerate_segformer_aug |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0362 |
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- Mean Iou: 0.3412 |
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- Mean Accuracy: 0.6823 |
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- Overall Accuracy: 0.6823 |
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- Accuracy Background: nan |
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- Accuracy Crack: 0.6823 |
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- Iou Background: 0.0 |
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- Iou Crack: 0.6823 |
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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: 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: 1 |
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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 Crack | Iou Background | Iou Crack | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| |
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| 0.0323 | 0.14 | 1000 | 0.0445 | 0.3573 | 0.7146 | 0.7146 | nan | 0.7146 | 0.0 | 0.7146 | |
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| 0.0222 | 0.27 | 2000 | 0.0394 | 0.3591 | 0.7181 | 0.7181 | nan | 0.7181 | 0.0 | 0.7181 | |
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| 0.0335 | 0.41 | 3000 | 0.0404 | 0.2907 | 0.5813 | 0.5813 | nan | 0.5813 | 0.0 | 0.5813 | |
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| 0.013 | 0.54 | 4000 | 0.0384 | 0.3244 | 0.6489 | 0.6489 | nan | 0.6489 | 0.0 | 0.6489 | |
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| 0.0159 | 0.68 | 5000 | 0.0382 | 0.3088 | 0.6176 | 0.6176 | nan | 0.6176 | 0.0 | 0.6176 | |
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| 0.0608 | 0.81 | 6000 | 0.0366 | 0.3251 | 0.6502 | 0.6502 | nan | 0.6502 | 0.0 | 0.6502 | |
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| 0.1738 | 0.95 | 7000 | 0.0362 | 0.3412 | 0.6823 | 0.6823 | nan | 0.6823 | 0.0 | 0.6823 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
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