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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 |
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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-patch16-224 |
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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: 0.9359420289855073 |
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- name: F1 |
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type: f1 |
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value: 0.8380952380952381 |
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- name: Precision |
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type: precision |
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value: 0.8895800933125972 |
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- name: Recall |
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type: recall |
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value: 0.7922437673130194 |
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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-patch16-224 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1562 |
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- Accuracy: 0.9359 |
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- F1: 0.8381 |
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- Precision: 0.8896 |
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- Recall: 0.7922 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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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| 0.3416 | 1.1494 | 100 | 0.3273 | 0.8771 | 0.6124 | 0.9005 | 0.4640 | |
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| 0.2215 | 2.2989 | 200 | 0.2187 | 0.9183 | 0.7902 | 0.8537 | 0.7355 | |
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| 0.1753 | 3.4483 | 300 | 0.1899 | 0.9238 | 0.8098 | 0.8472 | 0.7756 | |
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| 0.1656 | 4.5977 | 400 | 0.1732 | 0.9272 | 0.8175 | 0.8606 | 0.7784 | |
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| 0.1288 | 5.7471 | 500 | 0.1562 | 0.9359 | 0.8381 | 0.8896 | 0.7922 | |
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| 0.1323 | 6.8966 | 600 | 0.1597 | 0.9322 | 0.8326 | 0.8609 | 0.8061 | |
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| 0.1004 | 8.0460 | 700 | 0.1613 | 0.9316 | 0.8324 | 0.8542 | 0.8116 | |
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| 0.0956 | 9.1954 | 800 | 0.1612 | 0.9336 | 0.8368 | 0.8620 | 0.8130 | |
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| 0.0841 | 10.3448 | 900 | 0.1621 | 0.9345 | 0.8383 | 0.8669 | 0.8116 | |
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| 0.0764 | 11.4943 | 1000 | 0.1586 | 0.9359 | 0.8438 | 0.8615 | 0.8269 | |
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| 0.0726 | 12.6437 | 1100 | 0.1546 | 0.9420 | 0.8594 | 0.8729 | 0.8463 | |
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| 0.0732 | 13.7931 | 1200 | 0.1529 | 0.9409 | 0.8565 | 0.87 | 0.8435 | |
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| 0.0626 | 14.9425 | 1300 | 0.1589 | 0.9377 | 0.8485 | 0.8637 | 0.8338 | |
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| 0.0481 | 16.0920 | 1400 | 0.1612 | 0.9394 | 0.8510 | 0.8767 | 0.8269 | |
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| 0.0507 | 17.2414 | 1500 | 0.1679 | 0.9339 | 0.8394 | 0.8539 | 0.8255 | |
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| 0.0446 | 18.3908 | 1600 | 0.1623 | 0.9417 | 0.8597 | 0.8664 | 0.8532 | |
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| 0.0498 | 19.5402 | 1700 | 0.1625 | 0.9417 | 0.8601 | 0.8643 | 0.8560 | |
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| 0.0458 | 20.6897 | 1800 | 0.1601 | 0.9397 | 0.8533 | 0.8693 | 0.8380 | |
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| 0.0307 | 21.8391 | 1900 | 0.1626 | 0.9432 | 0.8637 | 0.8673 | 0.8601 | |
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| 0.0334 | 22.9885 | 2000 | 0.1621 | 0.9443 | 0.8642 | 0.8829 | 0.8463 | |
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| 0.0339 | 24.1379 | 2100 | 0.1680 | 0.9435 | 0.8645 | 0.8675 | 0.8615 | |
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| 0.0222 | 25.2874 | 2200 | 0.1656 | 0.9394 | 0.8537 | 0.8628 | 0.8449 | |
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| 0.026 | 26.4368 | 2300 | 0.1687 | 0.9386 | 0.8515 | 0.8612 | 0.8421 | |
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| 0.0353 | 27.5862 | 2400 | 0.1666 | 0.9403 | 0.8555 | 0.8665 | 0.8449 | |
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| 0.0294 | 28.7356 | 2500 | 0.1660 | 0.9429 | 0.8614 | 0.8755 | 0.8476 | |
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| 0.0243 | 29.8851 | 2600 | 0.1664 | 0.9423 | 0.8590 | 0.8795 | 0.8393 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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