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hushem_5x_beit_base_sgd_00001_fold2

Image Classification
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  2. model.safetensors +1 -1
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_sgd_00001_fold2
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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: 0.26666666666666666
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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_sgd_00001_fold2
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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: 1.5367
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+ - Accuracy: 0.2667
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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: 1e-05
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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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+ | 1.5006 | 1.0 | 27 | 1.5552 | 0.2667 |
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+ | 1.5759 | 2.0 | 54 | 1.5543 | 0.2667 |
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+ | 1.5707 | 3.0 | 81 | 1.5535 | 0.2667 |
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+ | 1.578 | 4.0 | 108 | 1.5527 | 0.2667 |
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+ | 1.5119 | 5.0 | 135 | 1.5520 | 0.2667 |
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+ | 1.5352 | 6.0 | 162 | 1.5512 | 0.2667 |
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+ | 1.5348 | 7.0 | 189 | 1.5504 | 0.2667 |
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+ | 1.5693 | 8.0 | 216 | 1.5497 | 0.2667 |
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+ | 1.5386 | 9.0 | 243 | 1.5490 | 0.2667 |
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+ | 1.5189 | 10.0 | 270 | 1.5483 | 0.2667 |
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+ | 1.5597 | 11.0 | 297 | 1.5477 | 0.2667 |
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+ | 1.5706 | 12.0 | 324 | 1.5471 | 0.2667 |
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+ | 1.5157 | 13.0 | 351 | 1.5465 | 0.2667 |
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+ | 1.5457 | 14.0 | 378 | 1.5458 | 0.2667 |
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+ | 1.5087 | 15.0 | 405 | 1.5453 | 0.2667 |
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+ | 1.5323 | 16.0 | 432 | 1.5447 | 0.2667 |
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+ | 1.5363 | 17.0 | 459 | 1.5442 | 0.2667 |
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+ | 1.5615 | 18.0 | 486 | 1.5437 | 0.2667 |
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+ | 1.5236 | 19.0 | 513 | 1.5433 | 0.2667 |
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+ | 1.566 | 20.0 | 540 | 1.5428 | 0.2667 |
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+ | 1.5446 | 21.0 | 567 | 1.5424 | 0.2667 |
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+ | 1.5289 | 22.0 | 594 | 1.5419 | 0.2667 |
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+ | 1.4823 | 23.0 | 621 | 1.5415 | 0.2667 |
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+ | 1.5025 | 24.0 | 648 | 1.5411 | 0.2667 |
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+ | 1.5362 | 25.0 | 675 | 1.5407 | 0.2667 |
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+ | 1.5593 | 26.0 | 702 | 1.5404 | 0.2667 |
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+ | 1.5515 | 27.0 | 729 | 1.5401 | 0.2667 |
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+ | 1.5275 | 28.0 | 756 | 1.5397 | 0.2667 |
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+ | 1.5171 | 29.0 | 783 | 1.5394 | 0.2667 |
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+ | 1.5816 | 30.0 | 810 | 1.5391 | 0.2667 |
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+ | 1.5294 | 31.0 | 837 | 1.5389 | 0.2667 |
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+ | 1.5276 | 32.0 | 864 | 1.5386 | 0.2667 |
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+ | 1.5584 | 33.0 | 891 | 1.5384 | 0.2667 |
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+ | 1.5549 | 34.0 | 918 | 1.5382 | 0.2667 |
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+ | 1.4864 | 35.0 | 945 | 1.5380 | 0.2667 |
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+ | 1.4851 | 36.0 | 972 | 1.5378 | 0.2667 |
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+ | 1.4835 | 37.0 | 999 | 1.5376 | 0.2667 |
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+ | 1.5708 | 38.0 | 1026 | 1.5374 | 0.2667 |
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+ | 1.5448 | 39.0 | 1053 | 1.5373 | 0.2667 |
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+ | 1.4945 | 40.0 | 1080 | 1.5372 | 0.2667 |
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+ | 1.486 | 41.0 | 1107 | 1.5371 | 0.2667 |
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+ | 1.5082 | 42.0 | 1134 | 1.5370 | 0.2667 |
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+ | 1.5323 | 43.0 | 1161 | 1.5369 | 0.2667 |
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+ | 1.4965 | 44.0 | 1188 | 1.5368 | 0.2667 |
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+ | 1.5407 | 45.0 | 1215 | 1.5368 | 0.2667 |
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+ | 1.5084 | 46.0 | 1242 | 1.5368 | 0.2667 |
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+ | 1.5191 | 47.0 | 1269 | 1.5367 | 0.2667 |
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+ | 1.5617 | 48.0 | 1296 | 1.5367 | 0.2667 |
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+ | 1.4992 | 49.0 | 1323 | 1.5367 | 0.2667 |
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+ | 1.4782 | 50.0 | 1350 | 1.5367 | 0.2667 |
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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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