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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-patch16-384 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- webdataset |
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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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model-index: |
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- name: frost-vision-v2-google_vit-base-patch16-384-v2024-11-10 |
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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: 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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- name: Accuracy |
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type: accuracy |
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value: 0.9274647887323944 |
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- name: F1 |
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type: f1 |
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value: 0.8186619718309859 |
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- name: Precision |
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type: precision |
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value: 0.8172231985940246 |
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- name: Recall |
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type: recall |
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value: 0.8201058201058201 |
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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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# frost-vision-v2-google_vit-base-patch16-384-v2024-11-10 |
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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: 0.1847 |
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- Accuracy: 0.9275 |
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- F1: 0.8187 |
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- Precision: 0.8172 |
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- Recall: 0.8201 |
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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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| 0.2243 | 1.4085 | 100 | 0.2088 | 0.9243 | 0.7981 | 0.8534 | 0.7496 | |
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| 0.2438 | 2.8169 | 200 | 0.1819 | 0.9299 | 0.8103 | 0.8817 | 0.7496 | |
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| 0.1338 | 4.2254 | 300 | 0.1608 | 0.9377 | 0.8449 | 0.8397 | 0.8501 | |
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| 0.1224 | 5.6338 | 400 | 0.1735 | 0.9271 | 0.8179 | 0.8158 | 0.8201 | |
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| 0.1065 | 7.0423 | 500 | 0.1847 | 0.9275 | 0.8187 | 0.8172 | 0.8201 | |
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| 0.1008 | 8.4507 | 600 | 0.1710 | 0.9405 | 0.8506 | 0.8528 | 0.8483 | |
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| 0.1005 | 9.8592 | 700 | 0.1823 | 0.9384 | 0.8405 | 0.8698 | 0.8131 | |
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| 0.0756 | 11.2676 | 800 | 0.1771 | 0.9415 | 0.8520 | 0.8613 | 0.8430 | |
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| 0.0653 | 12.6761 | 900 | 0.1971 | 0.9324 | 0.8310 | 0.8295 | 0.8325 | |
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| 0.0367 | 14.0845 | 1000 | 0.2123 | 0.9296 | 0.8221 | 0.8294 | 0.8148 | |
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| 0.0459 | 15.4930 | 1100 | 0.2006 | 0.9335 | 0.832 | 0.8387 | 0.8254 | |
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| 0.0559 | 16.9014 | 1200 | 0.2097 | 0.9313 | 0.8232 | 0.8470 | 0.8007 | |
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| 0.0382 | 18.3099 | 1300 | 0.2055 | 0.9352 | 0.8372 | 0.8401 | 0.8342 | |
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| 0.0361 | 19.7183 | 1400 | 0.2070 | 0.9335 | 0.8305 | 0.8449 | 0.8166 | |
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| 0.0358 | 21.1268 | 1500 | 0.1959 | 0.9398 | 0.8458 | 0.8653 | 0.8272 | |
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| 0.0382 | 22.5352 | 1600 | 0.2097 | 0.9320 | 0.8269 | 0.8412 | 0.8131 | |
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| 0.0285 | 23.9437 | 1700 | 0.2016 | 0.9415 | 0.8515 | 0.8639 | 0.8395 | |
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| 0.0141 | 25.3521 | 1800 | 0.2161 | 0.9366 | 0.8384 | 0.8537 | 0.8236 | |
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| 0.0179 | 26.7606 | 1900 | 0.2073 | 0.9377 | 0.8427 | 0.8495 | 0.8360 | |
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| 0.0263 | 28.1690 | 2000 | 0.2097 | 0.9391 | 0.8457 | 0.8556 | 0.8360 | |
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| 0.0191 | 29.5775 | 2100 | 0.2101 | 0.9377 | 0.8415 | 0.8545 | 0.8289 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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