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hushem_5x_beit_base_rms_00001_fold4

Image Classification
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beit
Generated from Trainer
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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_rms_00001_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: 0.9047619047619048
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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_rms_00001_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: 0.4242
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+ - Accuracy: 0.9048
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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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+ | 0.7963 | 1.0 | 28 | 0.5873 | 0.8095 |
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+ | 0.1378 | 2.0 | 56 | 0.2600 | 0.9048 |
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+ | 0.0372 | 3.0 | 84 | 0.1249 | 0.9286 |
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+ | 0.0142 | 4.0 | 112 | 0.1881 | 0.9048 |
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+ | 0.0031 | 5.0 | 140 | 0.2720 | 0.9524 |
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+ | 0.0011 | 6.0 | 168 | 0.2309 | 0.9286 |
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+ | 0.0018 | 7.0 | 196 | 0.3809 | 0.9048 |
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+ | 0.0008 | 8.0 | 224 | 0.3332 | 0.9048 |
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+ | 0.0014 | 9.0 | 252 | 0.3365 | 0.8810 |
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+ | 0.0123 | 10.0 | 280 | 0.2089 | 0.9286 |
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+ | 0.0005 | 11.0 | 308 | 0.1962 | 0.9286 |
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+ | 0.0038 | 12.0 | 336 | 0.2845 | 0.9048 |
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+ | 0.0078 | 13.0 | 364 | 0.2498 | 0.9048 |
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+ | 0.001 | 14.0 | 392 | 0.0353 | 1.0 |
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+ | 0.0002 | 15.0 | 420 | 0.1604 | 0.9286 |
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+ | 0.0003 | 16.0 | 448 | 0.6770 | 0.8810 |
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+ | 0.0002 | 17.0 | 476 | 0.3566 | 0.9048 |
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+ | 0.0001 | 18.0 | 504 | 0.1974 | 0.8810 |
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+ | 0.0004 | 19.0 | 532 | 0.0247 | 1.0 |
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+ | 0.0001 | 20.0 | 560 | 0.0905 | 0.9286 |
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+ | 0.0001 | 21.0 | 588 | 0.1806 | 0.9286 |
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+ | 0.0011 | 22.0 | 616 | 0.2156 | 0.9524 |
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+ | 0.0007 | 23.0 | 644 | 0.4203 | 0.9286 |
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+ | 0.0002 | 24.0 | 672 | 0.2731 | 0.9286 |
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+ | 0.0054 | 25.0 | 700 | 0.2589 | 0.8810 |
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+ | 0.0001 | 26.0 | 728 | 0.2893 | 0.9048 |
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+ | 0.0 | 27.0 | 756 | 0.3737 | 0.8810 |
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+ | 0.0002 | 28.0 | 784 | 0.3310 | 0.9048 |
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+ | 0.0001 | 29.0 | 812 | 0.2394 | 0.9048 |
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+ | 0.0 | 30.0 | 840 | 0.2320 | 0.9048 |
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+ | 0.0001 | 31.0 | 868 | 0.2751 | 0.9048 |
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+ | 0.0012 | 32.0 | 896 | 0.2756 | 0.9048 |
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+ | 0.0 | 33.0 | 924 | 0.1983 | 0.9048 |
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+ | 0.0001 | 34.0 | 952 | 0.1565 | 0.9048 |
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+ | 0.0 | 35.0 | 980 | 0.1912 | 0.9048 |
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+ | 0.0001 | 36.0 | 1008 | 0.2103 | 0.9048 |
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+ | 0.0 | 37.0 | 1036 | 0.1693 | 0.9048 |
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+ | 0.0 | 38.0 | 1064 | 0.1895 | 0.9048 |
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+ | 0.0 | 39.0 | 1092 | 0.2300 | 0.9048 |
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+ | 0.0018 | 40.0 | 1120 | 0.7391 | 0.9048 |
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+ | 0.0 | 41.0 | 1148 | 0.6660 | 0.9048 |
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+ | 0.0 | 42.0 | 1176 | 0.5981 | 0.9048 |
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+ | 0.0001 | 43.0 | 1204 | 0.6379 | 0.9048 |
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+ | 0.0001 | 44.0 | 1232 | 0.5736 | 0.9048 |
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+ | 0.0002 | 45.0 | 1260 | 0.4940 | 0.9048 |
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+ | 0.0001 | 46.0 | 1288 | 0.4348 | 0.9048 |
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+ | 0.0001 | 47.0 | 1316 | 0.4551 | 0.9048 |
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+ | 0.0 | 48.0 | 1344 | 0.4241 | 0.9048 |
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+ | 0.0026 | 49.0 | 1372 | 0.4242 | 0.9048 |
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+ | 0.0 | 50.0 | 1400 | 0.4242 | 0.9048 |
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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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