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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: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: classifier-posterior-glare-removal |
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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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# classifier-posterior-glare-removal |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the classifier_posterior_glare_removal_256_crop_s1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4990 |
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- Accuracy: 0.8593 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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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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| 0.626 | 0.8065 | 50 | 0.5622 | 0.7582 | |
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| 0.4848 | 1.6129 | 100 | 0.5952 | 0.6675 | |
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| 0.2195 | 2.4194 | 150 | 0.5258 | 0.8325 | |
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| 0.1967 | 3.2258 | 200 | 0.5911 | 0.7960 | |
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| 0.2945 | 4.0323 | 250 | 0.4966 | 0.8300 | |
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| 0.1866 | 4.8387 | 300 | 0.5222 | 0.8350 | |
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| 0.1211 | 5.6452 | 350 | 0.5328 | 0.8426 | |
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| 0.1666 | 6.4516 | 400 | 0.5545 | 0.8426 | |
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| 0.0737 | 7.2581 | 450 | 0.5327 | 0.8526 | |
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| 0.0314 | 8.0645 | 500 | 0.5208 | 0.8526 | |
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| 0.0329 | 8.8710 | 550 | 0.5773 | 0.8489 | |
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| 0.0497 | 9.6774 | 600 | 0.5994 | 0.8489 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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