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hushem_5x_beit_base_rms_001_fold1

Image Classification
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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_001_fold1
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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: 0.4444444444444444
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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_001_fold1
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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: 2.2430
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+ - Accuracy: 0.4444
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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: 0.001
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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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+ | 1.5782 | 1.0 | 27 | 1.4061 | 0.2444 |
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+ | 1.4004 | 2.0 | 54 | 1.4559 | 0.2444 |
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+ | 1.3873 | 3.0 | 81 | 1.4120 | 0.2444 |
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+ | 1.3666 | 4.0 | 108 | 1.6275 | 0.2444 |
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+ | 1.3597 | 5.0 | 135 | 1.4398 | 0.2444 |
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+ | 1.2814 | 6.0 | 162 | 1.5328 | 0.2444 |
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+ | 1.2056 | 7.0 | 189 | 1.5389 | 0.2 |
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+ | 1.1635 | 8.0 | 216 | 1.5332 | 0.2444 |
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+ | 1.1235 | 9.0 | 243 | 1.6681 | 0.2444 |
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+ | 1.1484 | 10.0 | 270 | 1.6176 | 0.2667 |
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+ | 1.1757 | 11.0 | 297 | 1.6312 | 0.2444 |
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+ | 1.1297 | 12.0 | 324 | 1.5067 | 0.2444 |
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+ | 1.1448 | 13.0 | 351 | 1.5657 | 0.2444 |
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+ | 1.1725 | 14.0 | 378 | 1.5184 | 0.1556 |
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+ | 1.1591 | 15.0 | 405 | 1.5790 | 0.2444 |
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+ | 1.1549 | 16.0 | 432 | 1.5501 | 0.2444 |
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+ | 1.0865 | 17.0 | 459 | 1.5776 | 0.2444 |
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+ | 1.1351 | 18.0 | 486 | 1.6195 | 0.3111 |
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+ | 1.0974 | 19.0 | 513 | 1.5360 | 0.2444 |
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+ | 1.0992 | 20.0 | 540 | 1.5742 | 0.3111 |
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+ | 1.0894 | 21.0 | 567 | 1.4918 | 0.3778 |
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+ | 1.0557 | 22.0 | 594 | 1.5742 | 0.2444 |
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+ | 1.0574 | 23.0 | 621 | 1.5043 | 0.4222 |
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+ | 1.0148 | 24.0 | 648 | 1.3535 | 0.4222 |
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+ | 1.1133 | 25.0 | 675 | 1.4897 | 0.4 |
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+ | 1.02 | 26.0 | 702 | 1.4554 | 0.4222 |
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+ | 1.0107 | 27.0 | 729 | 1.4238 | 0.4 |
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+ | 0.9307 | 28.0 | 756 | 1.7644 | 0.3556 |
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+ | 0.8335 | 29.0 | 783 | 2.0253 | 0.3556 |
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+ | 0.8203 | 30.0 | 810 | 1.7990 | 0.3556 |
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+ | 0.7263 | 31.0 | 837 | 1.6909 | 0.3778 |
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+ | 0.8387 | 32.0 | 864 | 1.4758 | 0.4 |
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+ | 0.6837 | 33.0 | 891 | 2.1584 | 0.3556 |
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+ | 0.7155 | 34.0 | 918 | 1.7102 | 0.3778 |
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+ | 0.6349 | 35.0 | 945 | 1.1875 | 0.4667 |
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+ | 0.6331 | 36.0 | 972 | 1.9965 | 0.4222 |
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+ | 0.5871 | 37.0 | 999 | 1.7881 | 0.4 |
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+ | 0.595 | 38.0 | 1026 | 1.7629 | 0.4 |
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+ | 0.5266 | 39.0 | 1053 | 1.6720 | 0.4222 |
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+ | 0.4985 | 40.0 | 1080 | 2.3229 | 0.4222 |
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+ | 0.4855 | 41.0 | 1107 | 1.6470 | 0.4444 |
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+ | 0.503 | 42.0 | 1134 | 1.7515 | 0.4667 |
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+ | 0.4432 | 43.0 | 1161 | 2.0538 | 0.4222 |
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+ | 0.3668 | 44.0 | 1188 | 2.1471 | 0.4444 |
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+ | 0.3654 | 45.0 | 1215 | 2.0004 | 0.4444 |
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+ | 0.3317 | 46.0 | 1242 | 2.1973 | 0.4444 |
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+ | 0.2413 | 47.0 | 1269 | 2.2882 | 0.4444 |
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+ | 0.2395 | 48.0 | 1296 | 2.2389 | 0.4444 |
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+ | 0.2502 | 49.0 | 1323 | 2.2430 | 0.4444 |
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+ | 0.237 | 50.0 | 1350 | 2.2430 | 0.4444 |
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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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