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BTX24
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deit-base-patch16-224-finetuned-stroke-binary

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  1. README.md +111 -0
  2. model.safetensors +1 -1
  3. runs/Mar16_19-32-23_774ccf98b3fe/events.out.tfevents.1742153554.774ccf98b3fe.5066.27 +2 -2
  4. runs/Mar16_19-32-23_774ccf98b3fe/events.out.tfevents.1742156353.774ccf98b3fe.5066.28 +3 -0
README.md ADDED
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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/deit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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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: deit-base-patch16-224-finetuned-stroke-binary
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+ results: []
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+ ---
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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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+
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+ # deit-base-patch16-224-finetuned-stroke-binary
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+
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+ This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1572
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+ - Accuracy: 0.9412
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+ - F1: 0.9407
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+ - Precision: 0.9419
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+ - Recall: 0.9412
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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+ - optimizer: Use OptimizerNames.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: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 48
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.7318 | 0.6202 | 100 | 0.7300 | 0.4509 | 0.3933 | 0.5863 | 0.4509 |
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+ | 0.6751 | 1.2357 | 200 | 0.6745 | 0.5699 | 0.5670 | 0.5647 | 0.5699 |
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+ | 0.6521 | 1.8558 | 300 | 0.6379 | 0.6431 | 0.5742 | 0.6402 | 0.6431 |
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+ | 0.5941 | 2.4713 | 400 | 0.5868 | 0.7010 | 0.6588 | 0.7256 | 0.7010 |
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+ | 0.5435 | 3.0868 | 500 | 0.5232 | 0.7445 | 0.7133 | 0.7816 | 0.7445 |
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+ | 0.4554 | 3.7070 | 600 | 0.4618 | 0.7820 | 0.7602 | 0.8189 | 0.7820 |
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+ | 0.3992 | 4.3225 | 700 | 0.3778 | 0.8399 | 0.8327 | 0.8519 | 0.8399 |
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+ | 0.3563 | 4.9426 | 800 | 0.3372 | 0.8494 | 0.8434 | 0.8596 | 0.8494 |
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+ | 0.3286 | 5.5581 | 900 | 0.2941 | 0.8810 | 0.8785 | 0.8846 | 0.8810 |
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+ | 0.2749 | 6.1736 | 1000 | 0.2696 | 0.8874 | 0.8854 | 0.8895 | 0.8874 |
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+ | 0.2687 | 6.7938 | 1100 | 0.2890 | 0.8788 | 0.8744 | 0.8901 | 0.8788 |
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+ | 0.26 | 7.4093 | 1200 | 0.2636 | 0.8901 | 0.8868 | 0.8988 | 0.8901 |
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+ | 0.2624 | 8.0248 | 1300 | 0.2342 | 0.9082 | 0.9071 | 0.9092 | 0.9082 |
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+ | 0.2853 | 8.6450 | 1400 | 0.2192 | 0.9132 | 0.9122 | 0.9143 | 0.9132 |
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+ | 0.2153 | 9.2605 | 1500 | 0.2269 | 0.9104 | 0.9090 | 0.9130 | 0.9104 |
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+ | 0.2288 | 9.8806 | 1600 | 0.2319 | 0.9082 | 0.9064 | 0.9124 | 0.9082 |
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+ | 0.2233 | 10.4961 | 1700 | 0.2089 | 0.9177 | 0.9165 | 0.9201 | 0.9177 |
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+ | 0.2006 | 11.1116 | 1800 | 0.2029 | 0.9209 | 0.9205 | 0.9207 | 0.9209 |
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+ | 0.2059 | 11.7318 | 1900 | 0.1981 | 0.9199 | 0.9196 | 0.9198 | 0.9199 |
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+ | 0.1993 | 12.3473 | 2000 | 0.2155 | 0.9168 | 0.9150 | 0.9220 | 0.9168 |
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+ | 0.1925 | 12.9674 | 2100 | 0.1921 | 0.9258 | 0.9249 | 0.9274 | 0.9258 |
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+ | 0.2067 | 13.5829 | 2200 | 0.1957 | 0.9267 | 0.9258 | 0.9286 | 0.9267 |
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+ | 0.1856 | 14.1984 | 2300 | 0.1927 | 0.9272 | 0.9261 | 0.9297 | 0.9272 |
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+ | 0.217 | 14.8186 | 2400 | 0.2155 | 0.9204 | 0.9188 | 0.9253 | 0.9204 |
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+ | 0.1895 | 15.4341 | 2500 | 0.1782 | 0.9349 | 0.9343 | 0.9357 | 0.9349 |
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+ | 0.2031 | 16.0496 | 2600 | 0.2666 | 0.8928 | 0.8888 | 0.9060 | 0.8928 |
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+ | 0.1853 | 16.6698 | 2700 | 0.1845 | 0.9335 | 0.9331 | 0.9339 | 0.9335 |
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+ | 0.1868 | 17.2853 | 2800 | 0.2151 | 0.9204 | 0.9185 | 0.9273 | 0.9204 |
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+ | 0.1725 | 17.9054 | 2900 | 0.1789 | 0.9335 | 0.9330 | 0.9341 | 0.9335 |
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+ | 0.1899 | 18.5209 | 3000 | 0.1704 | 0.9389 | 0.9384 | 0.9399 | 0.9389 |
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+ | 0.1614 | 19.1364 | 3100 | 0.1761 | 0.9353 | 0.9348 | 0.9362 | 0.9353 |
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+ | 0.166 | 19.7566 | 3200 | 0.1767 | 0.9362 | 0.9357 | 0.9372 | 0.9362 |
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+ | 0.1783 | 20.3721 | 3300 | 0.1584 | 0.9403 | 0.9401 | 0.9403 | 0.9403 |
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+ | 0.159 | 20.9922 | 3400 | 0.1572 | 0.9408 | 0.9403 | 0.9413 | 0.9408 |
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+ | 0.1668 | 21.6078 | 3500 | 0.1652 | 0.9426 | 0.9419 | 0.9442 | 0.9426 |
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+ | 0.1423 | 22.2233 | 3600 | 0.1601 | 0.9380 | 0.9376 | 0.9384 | 0.9380 |
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+ | 0.1713 | 22.8434 | 3700 | 0.1572 | 0.9421 | 0.9417 | 0.9428 | 0.9421 |
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+ | 0.1657 | 23.4589 | 3800 | 0.1579 | 0.9408 | 0.9403 | 0.9413 | 0.9408 |
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+ | 0.1424 | 24.0744 | 3900 | 0.1689 | 0.9403 | 0.9397 | 0.9417 | 0.9403 |
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+ | 0.169 | 24.6946 | 4000 | 0.1558 | 0.9444 | 0.9439 | 0.9451 | 0.9444 |
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+ | 0.1439 | 25.3101 | 4100 | 0.1572 | 0.9412 | 0.9407 | 0.9419 | 0.9412 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.3
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.4.0
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+ - Tokenizers 0.21.0
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