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hushem_5x_beit_base_rms_0001_fold3

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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_fold3
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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: 0.6744186046511628
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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_fold3
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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: 2.0271
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+ - Accuracy: 0.6744
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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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+ | 1.4187 | 1.0 | 28 | 1.4291 | 0.2558 |
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+ | 1.401 | 2.0 | 56 | 1.4569 | 0.2558 |
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+ | 1.367 | 3.0 | 84 | 1.2989 | 0.2791 |
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+ | 1.3068 | 4.0 | 112 | 1.1706 | 0.5116 |
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+ | 1.282 | 5.0 | 140 | 1.1869 | 0.5581 |
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+ | 1.1177 | 6.0 | 168 | 0.8916 | 0.7442 |
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+ | 0.8904 | 7.0 | 196 | 0.7798 | 0.7209 |
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+ | 0.9449 | 8.0 | 224 | 0.6587 | 0.7674 |
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+ | 0.8708 | 9.0 | 252 | 1.0524 | 0.5814 |
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+ | 0.9352 | 10.0 | 280 | 0.7664 | 0.6744 |
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+ | 0.8718 | 11.0 | 308 | 0.6191 | 0.7907 |
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+ | 0.7977 | 12.0 | 336 | 1.1991 | 0.6512 |
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+ | 0.8081 | 13.0 | 364 | 0.7062 | 0.7674 |
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+ | 0.7399 | 14.0 | 392 | 0.7130 | 0.6744 |
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+ | 0.8202 | 15.0 | 420 | 0.7484 | 0.6977 |
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+ | 0.7069 | 16.0 | 448 | 0.6665 | 0.6977 |
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+ | 0.6169 | 17.0 | 476 | 0.7828 | 0.6279 |
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+ | 0.6766 | 18.0 | 504 | 0.9849 | 0.5814 |
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+ | 0.6876 | 19.0 | 532 | 0.7015 | 0.7442 |
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+ | 0.5123 | 20.0 | 560 | 0.9230 | 0.7442 |
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+ | 0.4885 | 21.0 | 588 | 0.9671 | 0.6279 |
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+ | 0.5212 | 22.0 | 616 | 1.2712 | 0.6744 |
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+ | 0.5047 | 23.0 | 644 | 0.7902 | 0.6512 |
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+ | 0.4047 | 24.0 | 672 | 1.3996 | 0.7209 |
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+ | 0.361 | 25.0 | 700 | 1.1508 | 0.6279 |
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+ | 0.362 | 26.0 | 728 | 1.0709 | 0.6279 |
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+ | 0.3752 | 27.0 | 756 | 0.9894 | 0.6512 |
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+ | 0.2958 | 28.0 | 784 | 1.2219 | 0.6279 |
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+ | 0.3016 | 29.0 | 812 | 0.8154 | 0.6977 |
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+ | 0.2083 | 30.0 | 840 | 1.2432 | 0.6047 |
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+ | 0.2249 | 31.0 | 868 | 1.5401 | 0.6047 |
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+ | 0.1443 | 32.0 | 896 | 1.3193 | 0.6279 |
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+ | 0.1501 | 33.0 | 924 | 1.1707 | 0.6977 |
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+ | 0.1715 | 34.0 | 952 | 1.1677 | 0.7442 |
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+ | 0.2795 | 35.0 | 980 | 1.2992 | 0.6744 |
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+ | 0.1174 | 36.0 | 1008 | 1.6643 | 0.6744 |
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+ | 0.1132 | 37.0 | 1036 | 1.7522 | 0.6279 |
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+ | 0.0738 | 38.0 | 1064 | 1.6182 | 0.6744 |
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+ | 0.0433 | 39.0 | 1092 | 2.1223 | 0.6512 |
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+ | 0.0483 | 40.0 | 1120 | 2.5522 | 0.5814 |
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+ | 0.0333 | 41.0 | 1148 | 1.8374 | 0.6977 |
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+ | 0.0107 | 42.0 | 1176 | 1.9629 | 0.6744 |
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+ | 0.013 | 43.0 | 1204 | 1.6900 | 0.7209 |
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+ | 0.0316 | 44.0 | 1232 | 2.1881 | 0.6512 |
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+ | 0.0272 | 45.0 | 1260 | 1.8428 | 0.6744 |
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+ | 0.0298 | 46.0 | 1288 | 1.7049 | 0.7674 |
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+ | 0.0196 | 47.0 | 1316 | 1.9117 | 0.6744 |
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+ | 0.0084 | 48.0 | 1344 | 2.0336 | 0.6744 |
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+ | 0.0059 | 49.0 | 1372 | 2.0271 | 0.6744 |
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+ | 0.0065 | 50.0 | 1400 | 2.0271 | 0.6744 |
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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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