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--- |
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license: mit |
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base_model: shi-labs/nat-mini-in1k-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- image_folder |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: nat-mini-in1k-224-finetuned-breakhis |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: image_folder |
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type: image_folder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9669421487603306 |
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- name: F1 |
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type: f1 |
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value: 0.9612429172231991 |
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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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# nat-mini-in1k-224-finetuned-breakhis |
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This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) on the image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0983 |
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- Accuracy: 0.9669 |
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- F1: 0.9612 |
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- Roc Auc: 0.9648 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:| |
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| 0.3247 | 0.99 | 59 | 0.2084 | 0.9157 | 0.8968 | 0.8836 | |
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| 0.1338 | 2.0 | 119 | 0.1686 | 0.9355 | 0.9266 | 0.9437 | |
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| 0.1078 | 2.99 | 178 | 0.0986 | 0.9694 | 0.9636 | 0.9597 | |
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| 0.0795 | 4.0 | 238 | 0.0957 | 0.9719 | 0.9668 | 0.9660 | |
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| 0.0522 | 4.96 | 295 | 0.0983 | 0.9669 | 0.9612 | 0.9648 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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