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hushem_5x_beit_base_sgd_0001_fold5

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_sgd_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: 0.3170731707317073
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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_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: 1.4856
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+ - Accuracy: 0.3171
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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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+ | 1.5711 | 1.0 | 28 | 1.6258 | 0.2439 |
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+ | 1.5362 | 2.0 | 56 | 1.6161 | 0.2439 |
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+ | 1.5243 | 3.0 | 84 | 1.6077 | 0.2439 |
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+ | 1.5675 | 4.0 | 112 | 1.5988 | 0.2439 |
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+ | 1.5133 | 5.0 | 140 | 1.5920 | 0.2439 |
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+ | 1.5639 | 6.0 | 168 | 1.5854 | 0.2439 |
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+ | 1.555 | 7.0 | 196 | 1.5785 | 0.2439 |
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+ | 1.5064 | 8.0 | 224 | 1.5727 | 0.2439 |
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+ | 1.4878 | 9.0 | 252 | 1.5672 | 0.2439 |
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+ | 1.5121 | 10.0 | 280 | 1.5615 | 0.2439 |
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+ | 1.4492 | 11.0 | 308 | 1.5578 | 0.2439 |
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+ | 1.5023 | 12.0 | 336 | 1.5529 | 0.2439 |
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+ | 1.5035 | 13.0 | 364 | 1.5492 | 0.2439 |
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+ | 1.4801 | 14.0 | 392 | 1.5454 | 0.2439 |
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+ | 1.4838 | 15.0 | 420 | 1.5419 | 0.2683 |
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+ | 1.4587 | 16.0 | 448 | 1.5385 | 0.2683 |
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+ | 1.4655 | 17.0 | 476 | 1.5343 | 0.2683 |
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+ | 1.4244 | 18.0 | 504 | 1.5315 | 0.2927 |
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+ | 1.4339 | 19.0 | 532 | 1.5284 | 0.2927 |
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+ | 1.4266 | 20.0 | 560 | 1.5249 | 0.2927 |
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+ | 1.4474 | 21.0 | 588 | 1.5220 | 0.2927 |
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+ | 1.4652 | 22.0 | 616 | 1.5188 | 0.3171 |
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+ | 1.4621 | 23.0 | 644 | 1.5163 | 0.3171 |
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+ | 1.4655 | 24.0 | 672 | 1.5146 | 0.3171 |
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+ | 1.4192 | 25.0 | 700 | 1.5130 | 0.3171 |
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+ | 1.4459 | 26.0 | 728 | 1.5105 | 0.3171 |
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+ | 1.469 | 27.0 | 756 | 1.5090 | 0.3171 |
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+ | 1.3585 | 28.0 | 784 | 1.5067 | 0.3171 |
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+ | 1.4084 | 29.0 | 812 | 1.5049 | 0.3171 |
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+ | 1.4047 | 30.0 | 840 | 1.5031 | 0.3171 |
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+ | 1.4414 | 31.0 | 868 | 1.5013 | 0.3171 |
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+ | 1.3836 | 32.0 | 896 | 1.4995 | 0.3171 |
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+ | 1.3896 | 33.0 | 924 | 1.4979 | 0.3171 |
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+ | 1.4222 | 34.0 | 952 | 1.4964 | 0.3171 |
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+ | 1.4396 | 35.0 | 980 | 1.4952 | 0.3171 |
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+ | 1.3891 | 36.0 | 1008 | 1.4939 | 0.3171 |
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+ | 1.393 | 37.0 | 1036 | 1.4925 | 0.3171 |
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+ | 1.3697 | 38.0 | 1064 | 1.4914 | 0.3171 |
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+ | 1.4252 | 39.0 | 1092 | 1.4901 | 0.3171 |
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+ | 1.365 | 40.0 | 1120 | 1.4892 | 0.3171 |
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+ | 1.4164 | 41.0 | 1148 | 1.4883 | 0.3171 |
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+ | 1.3854 | 42.0 | 1176 | 1.4876 | 0.3171 |
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+ | 1.3744 | 43.0 | 1204 | 1.4870 | 0.3171 |
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+ | 1.4041 | 44.0 | 1232 | 1.4865 | 0.3171 |
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+ | 1.3952 | 45.0 | 1260 | 1.4861 | 0.3171 |
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+ | 1.3758 | 46.0 | 1288 | 1.4858 | 0.3171 |
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+ | 1.3986 | 47.0 | 1316 | 1.4857 | 0.3171 |
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+ | 1.3628 | 48.0 | 1344 | 1.4856 | 0.3171 |
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+ | 1.4108 | 49.0 | 1372 | 1.4856 | 0.3171 |
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+ | 1.4199 | 50.0 | 1400 | 1.4856 | 0.3171 |
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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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