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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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- 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: ecc_segformerv1 |
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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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# ecc_segformerv1 |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the rishitunu/ecc_crackdetector dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0351 |
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- Mean Iou: 0.9171 |
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- Mean Accuracy: 0.8041 |
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- Overall Accuracy: 0.8041 |
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- Accuracy Background: nan |
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- Accuracy Crack: 0.8041 |
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- Iou Background: 0.0 |
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- Iou Crack: 0.9171 |
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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: 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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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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