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hushem_5x_beit_base_rms_0001_fold5

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  2. model.safetensors +1 -1
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/beit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: hushem_5x_beit_base_rms_0001_fold5
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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: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: test
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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: 0.7073170731707317
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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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+ # hushem_5x_beit_base_rms_0001_fold5
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+
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.4047
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+ - Accuracy: 0.7073
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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: 0.0001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 50
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.4155 | 1.0 | 28 | 1.3777 | 0.2683 |
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+ | 1.3848 | 2.0 | 56 | 1.2989 | 0.2927 |
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+ | 1.3314 | 3.0 | 84 | 1.2733 | 0.4878 |
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+ | 1.2486 | 4.0 | 112 | 1.0811 | 0.5122 |
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+ | 1.2007 | 5.0 | 140 | 0.9236 | 0.5854 |
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+ | 1.05 | 6.0 | 168 | 1.1380 | 0.5122 |
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+ | 1.0162 | 7.0 | 196 | 0.9574 | 0.5854 |
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+ | 0.9476 | 8.0 | 224 | 1.4400 | 0.4878 |
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+ | 0.903 | 9.0 | 252 | 0.9012 | 0.6341 |
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+ | 0.9351 | 10.0 | 280 | 1.0183 | 0.6829 |
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+ | 0.8113 | 11.0 | 308 | 0.9612 | 0.6585 |
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+ | 0.8131 | 12.0 | 336 | 1.6631 | 0.4878 |
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+ | 0.7921 | 13.0 | 364 | 0.9316 | 0.6829 |
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+ | 0.8114 | 14.0 | 392 | 1.3372 | 0.5854 |
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+ | 0.7382 | 15.0 | 420 | 1.4796 | 0.6341 |
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+ | 0.7119 | 16.0 | 448 | 1.9753 | 0.5366 |
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+ | 0.6933 | 17.0 | 476 | 1.3458 | 0.7073 |
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+ | 0.591 | 18.0 | 504 | 1.3968 | 0.6585 |
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+ | 0.6986 | 19.0 | 532 | 1.4904 | 0.6829 |
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+ | 0.6832 | 20.0 | 560 | 1.7362 | 0.6585 |
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+ | 0.5173 | 21.0 | 588 | 1.5475 | 0.7317 |
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+ | 0.5116 | 22.0 | 616 | 1.9547 | 0.6585 |
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+ | 0.4833 | 23.0 | 644 | 2.1246 | 0.6341 |
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+ | 0.4295 | 24.0 | 672 | 1.9058 | 0.7317 |
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+ | 0.4431 | 25.0 | 700 | 2.4495 | 0.6585 |
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+ | 0.3801 | 26.0 | 728 | 1.6867 | 0.7561 |
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+ | 0.4263 | 27.0 | 756 | 2.1056 | 0.6585 |
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+ | 0.3209 | 28.0 | 784 | 2.6127 | 0.6098 |
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+ | 0.29 | 29.0 | 812 | 2.2833 | 0.6341 |
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+ | 0.2306 | 30.0 | 840 | 2.6477 | 0.6341 |
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+ | 0.2318 | 31.0 | 868 | 2.2205 | 0.6829 |
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+ | 0.1766 | 32.0 | 896 | 2.1057 | 0.8293 |
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+ | 0.1861 | 33.0 | 924 | 2.9102 | 0.6341 |
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+ | 0.2172 | 34.0 | 952 | 2.3319 | 0.7317 |
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+ | 0.1336 | 35.0 | 980 | 2.7931 | 0.7073 |
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+ | 0.128 | 36.0 | 1008 | 3.2544 | 0.6098 |
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+ | 0.1009 | 37.0 | 1036 | 2.3057 | 0.7805 |
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+ | 0.1495 | 38.0 | 1064 | 2.9047 | 0.7317 |
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+ | 0.0845 | 39.0 | 1092 | 3.1290 | 0.7317 |
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+ | 0.064 | 40.0 | 1120 | 2.9682 | 0.7561 |
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+ | 0.0399 | 41.0 | 1148 | 2.9364 | 0.7561 |
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+ | 0.0198 | 42.0 | 1176 | 4.0340 | 0.6585 |
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+ | 0.0179 | 43.0 | 1204 | 3.2313 | 0.7317 |
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+ | 0.0799 | 44.0 | 1232 | 3.4340 | 0.7317 |
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+ | 0.0495 | 45.0 | 1260 | 3.8737 | 0.6829 |
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+ | 0.041 | 46.0 | 1288 | 3.5139 | 0.6829 |
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+ | 0.0058 | 47.0 | 1316 | 3.4146 | 0.7073 |
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+ | 0.0141 | 48.0 | 1344 | 3.4016 | 0.7073 |
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+ | 0.0316 | 49.0 | 1372 | 3.4047 | 0.7073 |
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+ | 0.0269 | 50.0 | 1400 | 3.4047 | 0.7073 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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