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hushem_5x_deit_base_rms_001_fold2

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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: facebook/deit-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_deit_base_rms_001_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: 0.5333333333333333
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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_deit_base_rms_001_fold2
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+
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+ This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-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: 2.4335
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+ - Accuracy: 0.5333
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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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+ | 1.7708 | 1.0 | 27 | 1.4022 | 0.2444 |
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+ | 1.4249 | 2.0 | 54 | 1.3804 | 0.4444 |
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+ | 1.3964 | 3.0 | 81 | 1.3634 | 0.2667 |
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+ | 1.4087 | 4.0 | 108 | 1.4138 | 0.2444 |
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+ | 1.5106 | 5.0 | 135 | 1.3226 | 0.3333 |
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+ | 1.5674 | 6.0 | 162 | 1.3745 | 0.2444 |
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+ | 1.5358 | 7.0 | 189 | 1.3178 | 0.3778 |
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+ | 1.3201 | 8.0 | 216 | 1.0950 | 0.4 |
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+ | 1.624 | 9.0 | 243 | 1.3141 | 0.3111 |
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+ | 1.1174 | 10.0 | 270 | 1.4549 | 0.3778 |
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+ | 1.1475 | 11.0 | 297 | 0.9651 | 0.5778 |
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+ | 1.0882 | 12.0 | 324 | 0.9475 | 0.5778 |
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+ | 1.0589 | 13.0 | 351 | 1.0498 | 0.5111 |
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+ | 1.0658 | 14.0 | 378 | 0.9947 | 0.5333 |
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+ | 0.9897 | 15.0 | 405 | 0.9894 | 0.5333 |
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+ | 0.9767 | 16.0 | 432 | 0.9550 | 0.5778 |
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+ | 0.984 | 17.0 | 459 | 0.9380 | 0.5778 |
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+ | 1.0081 | 18.0 | 486 | 1.0509 | 0.4889 |
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+ | 0.8973 | 19.0 | 513 | 0.9732 | 0.4444 |
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+ | 0.8473 | 20.0 | 540 | 1.0049 | 0.4444 |
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+ | 0.7086 | 21.0 | 567 | 1.0847 | 0.4889 |
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+ | 0.7379 | 22.0 | 594 | 1.4535 | 0.4889 |
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+ | 0.7312 | 23.0 | 621 | 1.2763 | 0.5333 |
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+ | 0.6995 | 24.0 | 648 | 1.1444 | 0.3778 |
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+ | 0.6998 | 25.0 | 675 | 1.1643 | 0.3778 |
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+ | 0.7046 | 26.0 | 702 | 1.3603 | 0.5333 |
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+ | 0.6675 | 27.0 | 729 | 1.3027 | 0.6222 |
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+ | 0.6228 | 28.0 | 756 | 1.2068 | 0.4222 |
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+ | 0.5922 | 29.0 | 783 | 1.6511 | 0.5333 |
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+ | 0.6546 | 30.0 | 810 | 1.2512 | 0.4 |
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+ | 0.5393 | 31.0 | 837 | 1.4819 | 0.5333 |
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+ | 0.6185 | 32.0 | 864 | 1.3700 | 0.5111 |
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+ | 0.6184 | 33.0 | 891 | 1.5080 | 0.5556 |
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+ | 0.5907 | 34.0 | 918 | 1.4939 | 0.4222 |
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+ | 0.5753 | 35.0 | 945 | 1.4588 | 0.3556 |
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+ | 0.5557 | 36.0 | 972 | 1.4314 | 0.5111 |
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+ | 0.4886 | 37.0 | 999 | 1.8012 | 0.5556 |
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+ | 0.4981 | 38.0 | 1026 | 1.7648 | 0.5333 |
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+ | 0.4253 | 39.0 | 1053 | 1.7892 | 0.5556 |
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+ | 0.3579 | 40.0 | 1080 | 2.2102 | 0.5111 |
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+ | 0.4246 | 41.0 | 1107 | 1.6607 | 0.5556 |
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+ | 0.3838 | 42.0 | 1134 | 2.0356 | 0.5333 |
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+ | 0.3957 | 43.0 | 1161 | 2.0405 | 0.5111 |
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+ | 0.3149 | 44.0 | 1188 | 2.1882 | 0.5333 |
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+ | 0.3434 | 45.0 | 1215 | 2.2887 | 0.5333 |
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+ | 0.2478 | 46.0 | 1242 | 2.3165 | 0.5556 |
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+ | 0.2362 | 47.0 | 1269 | 2.4365 | 0.5333 |
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+ | 0.2191 | 48.0 | 1296 | 2.4233 | 0.5333 |
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+ | 0.1896 | 49.0 | 1323 | 2.4335 | 0.5333 |
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+ | 0.2369 | 50.0 | 1350 | 2.4335 | 0.5333 |
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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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