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hushem_1x_beit_base_rms_0001_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_1x_beit_base_rms_0001_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: 0.4222222222222222
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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_rms_0001_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: 1.7892
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+ - Accuracy: 0.4222
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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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+ | No log | 1.0 | 6 | 1.3881 | 0.2444 |
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+ | 1.983 | 2.0 | 12 | 1.4040 | 0.2444 |
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+ | 1.983 | 3.0 | 18 | 1.4052 | 0.2667 |
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+ | 1.41 | 4.0 | 24 | 1.3851 | 0.2444 |
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+ | 1.3993 | 5.0 | 30 | 1.3596 | 0.2667 |
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+ | 1.3993 | 6.0 | 36 | 1.5010 | 0.2444 |
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+ | 1.3135 | 7.0 | 42 | 1.4385 | 0.3778 |
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+ | 1.3135 | 8.0 | 48 | 1.3273 | 0.2222 |
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+ | 1.2878 | 9.0 | 54 | 1.7515 | 0.2444 |
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+ | 1.2036 | 10.0 | 60 | 1.4739 | 0.3111 |
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+ | 1.2036 | 11.0 | 66 | 1.4793 | 0.4444 |
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+ | 1.1544 | 12.0 | 72 | 1.6976 | 0.4444 |
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+ | 1.1544 | 13.0 | 78 | 1.5051 | 0.3778 |
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+ | 1.1611 | 14.0 | 84 | 2.0887 | 0.2444 |
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+ | 1.0944 | 15.0 | 90 | 1.7507 | 0.3778 |
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+ | 1.0944 | 16.0 | 96 | 1.5983 | 0.4 |
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+ | 1.1053 | 17.0 | 102 | 1.5239 | 0.3333 |
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+ | 1.1053 | 18.0 | 108 | 1.7239 | 0.3333 |
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+ | 0.9531 | 19.0 | 114 | 1.7796 | 0.3778 |
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+ | 0.9208 | 20.0 | 120 | 1.7000 | 0.4 |
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+ | 0.9208 | 21.0 | 126 | 1.5682 | 0.3556 |
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+ | 0.9119 | 22.0 | 132 | 1.6947 | 0.2889 |
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+ | 0.9119 | 23.0 | 138 | 1.9309 | 0.3111 |
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+ | 0.8438 | 24.0 | 144 | 1.7778 | 0.4 |
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+ | 0.7982 | 25.0 | 150 | 1.3358 | 0.4889 |
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+ | 0.7982 | 26.0 | 156 | 1.8930 | 0.3778 |
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+ | 0.7528 | 27.0 | 162 | 1.5978 | 0.4444 |
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+ | 0.7528 | 28.0 | 168 | 1.7048 | 0.4 |
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+ | 0.7372 | 29.0 | 174 | 1.4976 | 0.4 |
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+ | 0.6872 | 30.0 | 180 | 1.5193 | 0.4222 |
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+ | 0.6872 | 31.0 | 186 | 1.5712 | 0.3778 |
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+ | 0.6257 | 32.0 | 192 | 1.6492 | 0.4 |
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+ | 0.6257 | 33.0 | 198 | 1.6572 | 0.4444 |
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+ | 0.6115 | 34.0 | 204 | 1.7617 | 0.4222 |
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+ | 0.502 | 35.0 | 210 | 1.7836 | 0.4 |
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+ | 0.502 | 36.0 | 216 | 1.7245 | 0.4222 |
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+ | 0.5351 | 37.0 | 222 | 1.8523 | 0.3778 |
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+ | 0.5351 | 38.0 | 228 | 1.8752 | 0.3778 |
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+ | 0.4239 | 39.0 | 234 | 1.7739 | 0.4222 |
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+ | 0.4397 | 40.0 | 240 | 1.8121 | 0.4 |
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+ | 0.4397 | 41.0 | 246 | 1.7942 | 0.4222 |
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+ | 0.3888 | 42.0 | 252 | 1.7892 | 0.4222 |
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+ | 0.3888 | 43.0 | 258 | 1.7892 | 0.4222 |
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+ | 0.3836 | 44.0 | 264 | 1.7892 | 0.4222 |
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+ | 0.3564 | 45.0 | 270 | 1.7892 | 0.4222 |
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+ | 0.3564 | 46.0 | 276 | 1.7892 | 0.4222 |
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+ | 0.3801 | 47.0 | 282 | 1.7892 | 0.4222 |
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+ | 0.3801 | 48.0 | 288 | 1.7892 | 0.4222 |
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+ | 0.316 | 49.0 | 294 | 1.7892 | 0.4222 |
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+ | 0.3933 | 50.0 | 300 | 1.7892 | 0.4222 |
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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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