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hushem_5x_deit_small_rms_0001_fold5

Image Classification
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/deit-small-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_small_rms_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: 0.8048780487804879
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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_small_rms_0001_fold5
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+
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+ This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7179
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+ - Accuracy: 0.8049
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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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+ | 1.5878 | 1.0 | 28 | 1.4369 | 0.2439 |
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+ | 1.4242 | 2.0 | 56 | 1.4305 | 0.2683 |
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+ | 1.4275 | 3.0 | 84 | 1.4066 | 0.2439 |
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+ | 1.2279 | 4.0 | 112 | 0.9720 | 0.4878 |
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+ | 0.9327 | 5.0 | 140 | 1.0127 | 0.7561 |
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+ | 0.8019 | 6.0 | 168 | 0.6501 | 0.7073 |
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+ | 0.7585 | 7.0 | 196 | 0.8532 | 0.5854 |
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+ | 0.5196 | 8.0 | 224 | 0.7483 | 0.7561 |
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+ | 0.3986 | 9.0 | 252 | 0.5816 | 0.7805 |
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+ | 0.1892 | 10.0 | 280 | 0.8824 | 0.8049 |
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+ | 0.1797 | 11.0 | 308 | 0.6486 | 0.7561 |
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+ | 0.0426 | 12.0 | 336 | 0.7982 | 0.8049 |
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+ | 0.1197 | 13.0 | 364 | 0.9956 | 0.8049 |
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+ | 0.0493 | 14.0 | 392 | 1.3792 | 0.7805 |
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+ | 0.0017 | 15.0 | 420 | 1.2703 | 0.7317 |
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+ | 0.0006 | 16.0 | 448 | 1.3678 | 0.8049 |
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+ | 0.0001 | 17.0 | 476 | 1.3461 | 0.8049 |
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+ | 0.0001 | 18.0 | 504 | 1.3543 | 0.8049 |
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+ | 0.0001 | 19.0 | 532 | 1.3652 | 0.8049 |
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+ | 0.0001 | 20.0 | 560 | 1.3796 | 0.8049 |
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+ | 0.0001 | 21.0 | 588 | 1.3924 | 0.8049 |
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+ | 0.0001 | 22.0 | 616 | 1.4067 | 0.8049 |
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+ | 0.0 | 23.0 | 644 | 1.4230 | 0.8049 |
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+ | 0.0 | 24.0 | 672 | 1.4357 | 0.8049 |
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+ | 0.0 | 25.0 | 700 | 1.4503 | 0.8049 |
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+ | 0.0 | 26.0 | 728 | 1.4643 | 0.8049 |
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+ | 0.0 | 27.0 | 756 | 1.4784 | 0.8049 |
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+ | 0.0 | 28.0 | 784 | 1.4932 | 0.8049 |
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+ | 0.0 | 29.0 | 812 | 1.5093 | 0.8049 |
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+ | 0.0 | 30.0 | 840 | 1.5228 | 0.8049 |
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+ | 0.0 | 31.0 | 868 | 1.5390 | 0.8049 |
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+ | 0.0 | 32.0 | 896 | 1.5530 | 0.8049 |
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+ | 0.0 | 33.0 | 924 | 1.5639 | 0.8049 |
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+ | 0.0 | 34.0 | 952 | 1.5777 | 0.8049 |
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+ | 0.0 | 35.0 | 980 | 1.5911 | 0.8049 |
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+ | 0.0 | 36.0 | 1008 | 1.6043 | 0.8049 |
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+ | 0.0 | 37.0 | 1036 | 1.6184 | 0.8049 |
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+ | 0.0 | 38.0 | 1064 | 1.6314 | 0.8049 |
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+ | 0.0 | 39.0 | 1092 | 1.6443 | 0.8049 |
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+ | 0.0 | 40.0 | 1120 | 1.6568 | 0.8049 |
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+ | 0.0 | 41.0 | 1148 | 1.6687 | 0.8049 |
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+ | 0.0 | 42.0 | 1176 | 1.6794 | 0.8049 |
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+ | 0.0 | 43.0 | 1204 | 1.6881 | 0.8049 |
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+ | 0.0 | 44.0 | 1232 | 1.6961 | 0.8049 |
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+ | 0.0 | 45.0 | 1260 | 1.7042 | 0.8049 |
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+ | 0.0 | 46.0 | 1288 | 1.7106 | 0.8049 |
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+ | 0.0 | 47.0 | 1316 | 1.7158 | 0.8049 |
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+ | 0.0 | 48.0 | 1344 | 1.7178 | 0.8049 |
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+ | 0.0 | 49.0 | 1372 | 1.7179 | 0.8049 |
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+ | 0.0 | 50.0 | 1400 | 1.7179 | 0.8049 |
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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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