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hushem_1x_beit_base_sgd_00001_fold1

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_1x_beit_base_sgd_00001_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: 0.26666666666666666
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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_1x_beit_base_sgd_00001_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: 1.5985
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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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+ | No log | 1.0 | 6 | 1.6073 | 0.2667 |
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+ | 1.576 | 2.0 | 12 | 1.6069 | 0.2667 |
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+ | 1.576 | 3.0 | 18 | 1.6064 | 0.2667 |
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+ | 1.541 | 4.0 | 24 | 1.6060 | 0.2667 |
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+ | 1.5934 | 5.0 | 30 | 1.6056 | 0.2667 |
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+ | 1.5934 | 6.0 | 36 | 1.6052 | 0.2667 |
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+ | 1.5677 | 7.0 | 42 | 1.6048 | 0.2667 |
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+ | 1.5677 | 8.0 | 48 | 1.6044 | 0.2667 |
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+ | 1.5641 | 9.0 | 54 | 1.6041 | 0.2667 |
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+ | 1.5398 | 10.0 | 60 | 1.6037 | 0.2667 |
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+ | 1.5398 | 11.0 | 66 | 1.6034 | 0.2667 |
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+ | 1.568 | 12.0 | 72 | 1.6030 | 0.2667 |
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+ | 1.568 | 13.0 | 78 | 1.6027 | 0.2667 |
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+ | 1.5479 | 14.0 | 84 | 1.6024 | 0.2667 |
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+ | 1.5637 | 15.0 | 90 | 1.6021 | 0.2667 |
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+ | 1.5637 | 16.0 | 96 | 1.6019 | 0.2667 |
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+ | 1.5778 | 17.0 | 102 | 1.6016 | 0.2667 |
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+ | 1.5778 | 18.0 | 108 | 1.6013 | 0.2667 |
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+ | 1.5608 | 19.0 | 114 | 1.6011 | 0.2667 |
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+ | 1.5898 | 20.0 | 120 | 1.6009 | 0.2667 |
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+ | 1.5898 | 21.0 | 126 | 1.6006 | 0.2667 |
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+ | 1.5472 | 22.0 | 132 | 1.6004 | 0.2667 |
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+ | 1.5472 | 23.0 | 138 | 1.6002 | 0.2667 |
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+ | 1.5773 | 24.0 | 144 | 1.6000 | 0.2667 |
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+ | 1.5601 | 25.0 | 150 | 1.5998 | 0.2667 |
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+ | 1.5601 | 26.0 | 156 | 1.5997 | 0.2667 |
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+ | 1.5627 | 27.0 | 162 | 1.5995 | 0.2667 |
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+ | 1.5627 | 28.0 | 168 | 1.5994 | 0.2667 |
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+ | 1.5472 | 29.0 | 174 | 1.5993 | 0.2667 |
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+ | 1.5831 | 30.0 | 180 | 1.5991 | 0.2667 |
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+ | 1.5831 | 31.0 | 186 | 1.5990 | 0.2667 |
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+ | 1.5527 | 32.0 | 192 | 1.5989 | 0.2667 |
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+ | 1.5527 | 33.0 | 198 | 1.5988 | 0.2667 |
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+ | 1.535 | 34.0 | 204 | 1.5988 | 0.2667 |
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+ | 1.5751 | 35.0 | 210 | 1.5987 | 0.2667 |
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+ | 1.5751 | 36.0 | 216 | 1.5986 | 0.2667 |
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+ | 1.5377 | 37.0 | 222 | 1.5986 | 0.2667 |
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+ | 1.5377 | 38.0 | 228 | 1.5985 | 0.2667 |
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+ | 1.5661 | 39.0 | 234 | 1.5985 | 0.2667 |
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+ | 1.5536 | 40.0 | 240 | 1.5985 | 0.2667 |
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+ | 1.5536 | 41.0 | 246 | 1.5985 | 0.2667 |
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+ | 1.5592 | 42.0 | 252 | 1.5985 | 0.2667 |
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+ | 1.5592 | 43.0 | 258 | 1.5985 | 0.2667 |
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+ | 1.5823 | 44.0 | 264 | 1.5985 | 0.2667 |
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+ | 1.5597 | 45.0 | 270 | 1.5985 | 0.2667 |
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+ | 1.5597 | 46.0 | 276 | 1.5985 | 0.2667 |
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+ | 1.5427 | 47.0 | 282 | 1.5985 | 0.2667 |
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+ | 1.5427 | 48.0 | 288 | 1.5985 | 0.2667 |
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+ | 1.5619 | 49.0 | 294 | 1.5985 | 0.2667 |
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+ | 1.5725 | 50.0 | 300 | 1.5985 | 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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