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--- |
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base_model: google/vit-base-patch16-384 |
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datasets: |
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- webdataset |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: vit-base-patch16-384-finetuned_v2024-7-25-frost |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: webdataset |
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type: webdataset |
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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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- type: accuracy |
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value: 0.9746666666666667 |
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name: Accuracy |
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- type: f1 |
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value: 0.9372937293729373 |
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name: F1 |
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- type: precision |
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value: 0.9342105263157895 |
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name: Precision |
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- type: recall |
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value: 0.9403973509933775 |
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name: Recall |
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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-base-patch16-384-finetuned_v2024-7-25-frost |
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This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0795 |
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- Accuracy: 0.9747 |
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- F1: 0.9373 |
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- Precision: 0.9342 |
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- Recall: 0.9404 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.0624 | 1.7544 | 100 | 0.0458 | 0.9867 | 0.9665 | 0.9774 | 0.9558 | |
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| 0.0729 | 3.5088 | 200 | 0.0942 | 0.9689 | 0.9220 | 0.9303 | 0.9139 | |
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| 0.0566 | 5.2632 | 300 | 0.0802 | 0.972 | 0.9311 | 0.9221 | 0.9404 | |
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| 0.051 | 7.0175 | 400 | 0.0965 | 0.9631 | 0.9066 | 0.9243 | 0.8896 | |
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| 0.0686 | 8.7719 | 500 | 0.0795 | 0.9747 | 0.9373 | 0.9342 | 0.9404 | |
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| 0.0271 | 10.5263 | 600 | 0.0935 | 0.9693 | 0.9239 | 0.9229 | 0.9249 | |
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| 0.0273 | 12.2807 | 700 | 0.0975 | 0.9716 | 0.9300 | 0.9219 | 0.9382 | |
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| 0.0445 | 14.0351 | 800 | 0.0910 | 0.9698 | 0.9248 | 0.9268 | 0.9227 | |
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| 0.0217 | 15.7895 | 900 | 0.0942 | 0.9698 | 0.9243 | 0.9326 | 0.9161 | |
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| 0.0257 | 17.5439 | 1000 | 0.0906 | 0.9684 | 0.9210 | 0.9283 | 0.9139 | |
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| 0.0188 | 19.2982 | 1100 | 0.1028 | 0.9676 | 0.9181 | 0.9338 | 0.9029 | |
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| 0.0196 | 21.0526 | 1200 | 0.1020 | 0.9698 | 0.9244 | 0.9306 | 0.9183 | |
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| 0.025 | 22.8070 | 1300 | 0.1005 | 0.9702 | 0.9258 | 0.9289 | 0.9227 | |
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| 0.009 | 24.5614 | 1400 | 0.0976 | 0.9729 | 0.9324 | 0.9356 | 0.9294 | |
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| 0.0184 | 26.3158 | 1500 | 0.0987 | 0.9716 | 0.9290 | 0.9332 | 0.9249 | |
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| 0.0048 | 28.0702 | 1600 | 0.0958 | 0.972 | 0.9301 | 0.9353 | 0.9249 | |
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| 0.0072 | 29.8246 | 1700 | 0.0948 | 0.972 | 0.9301 | 0.9353 | 0.9249 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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