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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/vit-msn-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-msn-small-corect_dataset_lateral_flow_ivalidation |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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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: 0.9047619047619048 |
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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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# vit-msn-small-corect_dataset_lateral_flow_ivalidation |
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This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2930 |
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- Accuracy: 0.9048 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:| |
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| No log | 0.9231 | 3 | 0.6350 | 0.6337 | |
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| No log | 1.8462 | 6 | 0.5047 | 0.8022 | |
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| No log | 2.7692 | 9 | 0.3701 | 0.8791 | |
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| 0.5485 | 4.0 | 13 | 0.5379 | 0.7436 | |
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| 0.5485 | 4.9231 | 16 | 0.2748 | 0.8938 | |
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| 0.5485 | 5.8462 | 19 | 0.3004 | 0.8974 | |
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| 0.3335 | 6.7692 | 22 | 0.3492 | 0.8681 | |
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| 0.3335 | 8.0 | 26 | 0.2497 | 0.8974 | |
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| 0.3335 | 8.9231 | 29 | 0.4304 | 0.8315 | |
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| 0.3087 | 9.8462 | 32 | 0.3479 | 0.8791 | |
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| 0.3087 | 10.7692 | 35 | 0.3796 | 0.8645 | |
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| 0.3087 | 12.0 | 39 | 0.4152 | 0.8352 | |
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| 0.2614 | 12.9231 | 42 | 0.3199 | 0.9011 | |
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| 0.2614 | 13.8462 | 45 | 0.3434 | 0.8718 | |
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| 0.2614 | 14.7692 | 48 | 0.4001 | 0.8462 | |
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| 0.2471 | 16.0 | 52 | 0.3220 | 0.8901 | |
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| 0.2471 | 16.9231 | 55 | 0.3540 | 0.8718 | |
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| 0.2471 | 17.8462 | 58 | 0.4019 | 0.8535 | |
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| 0.2817 | 18.7692 | 61 | 0.3152 | 0.8974 | |
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| 0.2817 | 20.0 | 65 | 0.3978 | 0.8571 | |
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| 0.2817 | 20.9231 | 68 | 0.4289 | 0.8388 | |
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| 0.2353 | 21.8462 | 71 | 0.3146 | 0.8974 | |
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| 0.2353 | 22.7692 | 74 | 0.3206 | 0.8864 | |
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| 0.2353 | 24.0 | 78 | 0.3715 | 0.8828 | |
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| 0.2339 | 24.9231 | 81 | 0.3446 | 0.8938 | |
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| 0.2339 | 25.8462 | 84 | 0.2930 | 0.9048 | |
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| 0.2339 | 26.7692 | 87 | 0.4349 | 0.8205 | |
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| 0.2301 | 28.0 | 91 | 0.3630 | 0.8681 | |
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| 0.2301 | 28.9231 | 94 | 0.3669 | 0.8645 | |
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| 0.2301 | 29.8462 | 97 | 0.5037 | 0.7912 | |
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| 0.2115 | 30.7692 | 100 | 0.3449 | 0.8828 | |
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| 0.2115 | 32.0 | 104 | 0.3280 | 0.9011 | |
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| 0.2115 | 32.9231 | 107 | 0.4031 | 0.8425 | |
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| 0.2033 | 33.8462 | 110 | 0.3612 | 0.8535 | |
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| 0.2033 | 34.7692 | 113 | 0.3163 | 0.8901 | |
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| 0.2033 | 36.0 | 117 | 0.3234 | 0.8864 | |
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| 0.1807 | 36.9231 | 120 | 0.3307 | 0.8791 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.19.1 |
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