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hushem_1x_beit_base_rms_001_fold4

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_001_fold4
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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: 0.4523809523809524
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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_001_fold4
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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: 1.1403
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+ - Accuracy: 0.4524
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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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+ | No log | 1.0 | 6 | 2.4924 | 0.2619 |
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+ | 4.2258 | 2.0 | 12 | 2.2430 | 0.2381 |
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+ | 4.2258 | 3.0 | 18 | 1.7745 | 0.2619 |
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+ | 1.6665 | 4.0 | 24 | 1.4260 | 0.2381 |
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+ | 1.4856 | 5.0 | 30 | 1.3866 | 0.2619 |
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+ | 1.4856 | 6.0 | 36 | 1.4278 | 0.2619 |
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+ | 1.4454 | 7.0 | 42 | 1.4079 | 0.2381 |
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+ | 1.4454 | 8.0 | 48 | 1.4268 | 0.2381 |
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+ | 1.408 | 9.0 | 54 | 1.3464 | 0.3095 |
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+ | 1.4164 | 10.0 | 60 | 1.3818 | 0.2619 |
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+ | 1.4164 | 11.0 | 66 | 1.3229 | 0.4048 |
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+ | 1.3723 | 12.0 | 72 | 1.2005 | 0.4286 |
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+ | 1.3723 | 13.0 | 78 | 1.3168 | 0.3333 |
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+ | 1.3294 | 14.0 | 84 | 1.3652 | 0.2857 |
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+ | 1.3514 | 15.0 | 90 | 1.2992 | 0.3095 |
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+ | 1.3514 | 16.0 | 96 | 1.2709 | 0.3095 |
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+ | 1.2833 | 17.0 | 102 | 1.0901 | 0.5714 |
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+ | 1.2833 | 18.0 | 108 | 1.2138 | 0.4286 |
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+ | 1.2546 | 19.0 | 114 | 1.2470 | 0.3810 |
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+ | 1.2893 | 20.0 | 120 | 1.2665 | 0.4048 |
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+ | 1.2893 | 21.0 | 126 | 1.1295 | 0.5476 |
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+ | 1.2456 | 22.0 | 132 | 1.1935 | 0.4762 |
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+ | 1.2456 | 23.0 | 138 | 1.1859 | 0.2857 |
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+ | 1.2107 | 24.0 | 144 | 1.2333 | 0.3095 |
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+ | 1.211 | 25.0 | 150 | 1.1492 | 0.5 |
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+ | 1.211 | 26.0 | 156 | 1.1293 | 0.3810 |
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+ | 1.2139 | 27.0 | 162 | 1.1301 | 0.4048 |
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+ | 1.2139 | 28.0 | 168 | 1.2567 | 0.2857 |
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+ | 1.1599 | 29.0 | 174 | 1.1146 | 0.4524 |
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+ | 1.1826 | 30.0 | 180 | 1.1895 | 0.4524 |
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+ | 1.1826 | 31.0 | 186 | 1.1803 | 0.4286 |
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+ | 1.1665 | 32.0 | 192 | 1.1331 | 0.4524 |
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+ | 1.1665 | 33.0 | 198 | 1.2501 | 0.2619 |
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+ | 1.1881 | 34.0 | 204 | 1.1720 | 0.3571 |
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+ | 1.1428 | 35.0 | 210 | 1.1303 | 0.3810 |
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+ | 1.1428 | 36.0 | 216 | 1.0467 | 0.4524 |
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+ | 1.1325 | 37.0 | 222 | 1.1840 | 0.3095 |
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+ | 1.1325 | 38.0 | 228 | 1.1537 | 0.3571 |
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+ | 1.0868 | 39.0 | 234 | 1.1576 | 0.3571 |
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+ | 1.0845 | 40.0 | 240 | 1.1445 | 0.4524 |
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+ | 1.0845 | 41.0 | 246 | 1.1472 | 0.4524 |
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+ | 1.0808 | 42.0 | 252 | 1.1403 | 0.4524 |
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+ | 1.0808 | 43.0 | 258 | 1.1403 | 0.4524 |
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+ | 1.0575 | 44.0 | 264 | 1.1403 | 0.4524 |
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+ | 1.0837 | 45.0 | 270 | 1.1403 | 0.4524 |
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+ | 1.0837 | 46.0 | 276 | 1.1403 | 0.4524 |
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+ | 1.0819 | 47.0 | 282 | 1.1403 | 0.4524 |
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+ | 1.0819 | 48.0 | 288 | 1.1403 | 0.4524 |
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+ | 1.0729 | 49.0 | 294 | 1.1403 | 0.4524 |
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+ | 1.0942 | 50.0 | 300 | 1.1403 | 0.4524 |
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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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