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hushem_5x_deit_small_sgd_0001_fold3

Image Classification
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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-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_sgd_0001_fold3
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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: 0.20930232558139536
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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_sgd_0001_fold3
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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: 1.4264
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+ - Accuracy: 0.2093
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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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+ | 1.5059 | 1.0 | 28 | 1.5906 | 0.2093 |
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+ | 1.4969 | 2.0 | 56 | 1.5795 | 0.2093 |
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+ | 1.4242 | 3.0 | 84 | 1.5692 | 0.2093 |
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+ | 1.4628 | 4.0 | 112 | 1.5599 | 0.2093 |
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+ | 1.5851 | 5.0 | 140 | 1.5506 | 0.2093 |
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+ | 1.4881 | 6.0 | 168 | 1.5418 | 0.2093 |
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+ | 1.4744 | 7.0 | 196 | 1.5338 | 0.2326 |
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+ | 1.427 | 8.0 | 224 | 1.5266 | 0.2558 |
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+ | 1.4274 | 9.0 | 252 | 1.5192 | 0.2558 |
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+ | 1.4168 | 10.0 | 280 | 1.5125 | 0.2558 |
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+ | 1.4418 | 11.0 | 308 | 1.5064 | 0.2558 |
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+ | 1.4361 | 12.0 | 336 | 1.5007 | 0.2558 |
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+ | 1.4139 | 13.0 | 364 | 1.4950 | 0.2558 |
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+ | 1.3932 | 14.0 | 392 | 1.4898 | 0.2558 |
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+ | 1.4041 | 15.0 | 420 | 1.4850 | 0.2326 |
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+ | 1.3745 | 16.0 | 448 | 1.4806 | 0.2326 |
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+ | 1.3653 | 17.0 | 476 | 1.4764 | 0.2093 |
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+ | 1.3841 | 18.0 | 504 | 1.4723 | 0.2093 |
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+ | 1.3735 | 19.0 | 532 | 1.4687 | 0.2093 |
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+ | 1.3391 | 20.0 | 560 | 1.4653 | 0.2093 |
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+ | 1.3879 | 21.0 | 588 | 1.4620 | 0.2093 |
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+ | 1.3861 | 22.0 | 616 | 1.4589 | 0.2093 |
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+ | 1.3726 | 23.0 | 644 | 1.4561 | 0.2093 |
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+ | 1.3725 | 24.0 | 672 | 1.4534 | 0.2093 |
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+ | 1.3587 | 25.0 | 700 | 1.4508 | 0.2093 |
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+ | 1.3359 | 26.0 | 728 | 1.4485 | 0.2093 |
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+ | 1.3627 | 27.0 | 756 | 1.4462 | 0.2326 |
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+ | 1.3855 | 28.0 | 784 | 1.4442 | 0.2326 |
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+ | 1.353 | 29.0 | 812 | 1.4424 | 0.2093 |
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+ | 1.301 | 30.0 | 840 | 1.4407 | 0.2093 |
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+ | 1.3248 | 31.0 | 868 | 1.4390 | 0.2093 |
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+ | 1.3654 | 32.0 | 896 | 1.4375 | 0.2093 |
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+ | 1.364 | 33.0 | 924 | 1.4361 | 0.2093 |
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+ | 1.322 | 34.0 | 952 | 1.4347 | 0.2093 |
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+ | 1.3619 | 35.0 | 980 | 1.4335 | 0.2093 |
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+ | 1.3562 | 36.0 | 1008 | 1.4324 | 0.2093 |
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+ | 1.4034 | 37.0 | 1036 | 1.4314 | 0.2093 |
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+ | 1.3401 | 38.0 | 1064 | 1.4304 | 0.2093 |
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+ | 1.3307 | 39.0 | 1092 | 1.4297 | 0.2093 |
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+ | 1.3736 | 40.0 | 1120 | 1.4290 | 0.2093 |
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+ | 1.3675 | 41.0 | 1148 | 1.4284 | 0.2093 |
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+ | 1.3234 | 42.0 | 1176 | 1.4279 | 0.2093 |
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+ | 1.3321 | 43.0 | 1204 | 1.4274 | 0.2093 |
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+ | 1.3436 | 44.0 | 1232 | 1.4270 | 0.2093 |
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+ | 1.3719 | 45.0 | 1260 | 1.4268 | 0.2093 |
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+ | 1.3462 | 46.0 | 1288 | 1.4266 | 0.2093 |
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+ | 1.3448 | 47.0 | 1316 | 1.4265 | 0.2093 |
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+ | 1.3465 | 48.0 | 1344 | 1.4264 | 0.2093 |
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+ | 1.2951 | 49.0 | 1372 | 1.4264 | 0.2093 |
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+ | 1.3665 | 50.0 | 1400 | 1.4264 | 0.2093 |
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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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