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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_train_test |
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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_train_test |
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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: 0.0298 |
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- Mean Iou: 0.3639 |
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- Mean Accuracy: 0.7278 |
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- Overall Accuracy: 0.7278 |
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- Accuracy Background: nan |
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- Accuracy Crack: 0.7278 |
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- Iou Background: 0.0 |
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- Iou Crack: 0.7278 |
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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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| 0.0374 | 0.18 | 1000 | 0.0410 | 0.2472 | 0.4944 | 0.4944 | nan | 0.4944 | 0.0 | 0.4944 | |
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| 0.0337 | 0.36 | 2000 | 0.0341 | 0.3749 | 0.7497 | 0.7497 | nan | 0.7497 | 0.0 | 0.7497 | |
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| 0.0209 | 0.55 | 3000 | 0.0318 | 0.3335 | 0.6670 | 0.6670 | nan | 0.6670 | 0.0 | 0.6670 | |
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| 0.0099 | 0.73 | 4000 | 0.0315 | 0.3371 | 0.6743 | 0.6743 | nan | 0.6743 | 0.0 | 0.6743 | |
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| 0.026 | 0.91 | 5000 | 0.0298 | 0.3639 | 0.7278 | 0.7278 | nan | 0.7278 | 0.0 | 0.7278 | |
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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 2.14.0 |
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- Tokenizers 0.13.3 |
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