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hushem_5x_deit_small_sgd_00001_fold5

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_00001_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: 0.24390243902439024
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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_00001_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: 1.4968
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+ - Accuracy: 0.2439
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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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+ | 1.5975 | 1.0 | 28 | 1.5197 | 0.2195 |
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+ | 1.5191 | 2.0 | 56 | 1.5186 | 0.2195 |
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+ | 1.5652 | 3.0 | 84 | 1.5176 | 0.2195 |
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+ | 1.5368 | 4.0 | 112 | 1.5166 | 0.2195 |
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+ | 1.5533 | 5.0 | 140 | 1.5156 | 0.2195 |
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+ | 1.5934 | 6.0 | 168 | 1.5147 | 0.2195 |
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+ | 1.5997 | 7.0 | 196 | 1.5138 | 0.2195 |
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+ | 1.543 | 8.0 | 224 | 1.5129 | 0.2195 |
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+ | 1.5785 | 9.0 | 252 | 1.5120 | 0.2195 |
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+ | 1.5476 | 10.0 | 280 | 1.5112 | 0.2195 |
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+ | 1.5374 | 11.0 | 308 | 1.5104 | 0.2195 |
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+ | 1.5776 | 12.0 | 336 | 1.5096 | 0.2195 |
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+ | 1.552 | 13.0 | 364 | 1.5088 | 0.2195 |
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+ | 1.5084 | 14.0 | 392 | 1.5081 | 0.2195 |
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+ | 1.5475 | 15.0 | 420 | 1.5073 | 0.2195 |
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+ | 1.5527 | 16.0 | 448 | 1.5067 | 0.2195 |
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+ | 1.5461 | 17.0 | 476 | 1.5060 | 0.2195 |
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+ | 1.553 | 18.0 | 504 | 1.5054 | 0.2195 |
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+ | 1.5466 | 19.0 | 532 | 1.5047 | 0.2195 |
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+ | 1.5068 | 20.0 | 560 | 1.5041 | 0.2195 |
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+ | 1.5792 | 21.0 | 588 | 1.5036 | 0.2195 |
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+ | 1.5408 | 22.0 | 616 | 1.5030 | 0.2195 |
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+ | 1.4869 | 23.0 | 644 | 1.5025 | 0.2195 |
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+ | 1.5203 | 24.0 | 672 | 1.5020 | 0.2439 |
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+ | 1.5205 | 25.0 | 700 | 1.5016 | 0.2439 |
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+ | 1.5334 | 26.0 | 728 | 1.5011 | 0.2439 |
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+ | 1.5195 | 27.0 | 756 | 1.5007 | 0.2439 |
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+ | 1.555 | 28.0 | 784 | 1.5003 | 0.2439 |
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+ | 1.5231 | 29.0 | 812 | 1.4999 | 0.2439 |
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+ | 1.5521 | 30.0 | 840 | 1.4996 | 0.2439 |
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+ | 1.5405 | 31.0 | 868 | 1.4992 | 0.2439 |
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+ | 1.5223 | 32.0 | 896 | 1.4989 | 0.2439 |
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+ | 1.533 | 33.0 | 924 | 1.4986 | 0.2439 |
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+ | 1.5569 | 34.0 | 952 | 1.4984 | 0.2439 |
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+ | 1.5415 | 35.0 | 980 | 1.4981 | 0.2439 |
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+ | 1.5242 | 36.0 | 1008 | 1.4979 | 0.2439 |
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+ | 1.5342 | 37.0 | 1036 | 1.4977 | 0.2439 |
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+ | 1.51 | 38.0 | 1064 | 1.4975 | 0.2439 |
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+ | 1.4915 | 39.0 | 1092 | 1.4974 | 0.2439 |
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+ | 1.533 | 40.0 | 1120 | 1.4972 | 0.2439 |
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+ | 1.559 | 41.0 | 1148 | 1.4971 | 0.2439 |
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+ | 1.5496 | 42.0 | 1176 | 1.4970 | 0.2439 |
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+ | 1.5368 | 43.0 | 1204 | 1.4969 | 0.2439 |
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+ | 1.5602 | 44.0 | 1232 | 1.4969 | 0.2439 |
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+ | 1.5291 | 45.0 | 1260 | 1.4968 | 0.2439 |
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+ | 1.5316 | 46.0 | 1288 | 1.4968 | 0.2439 |
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+ | 1.5518 | 47.0 | 1316 | 1.4968 | 0.2439 |
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+ | 1.5141 | 48.0 | 1344 | 1.4968 | 0.2439 |
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+ | 1.515 | 49.0 | 1372 | 1.4968 | 0.2439 |
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+ | 1.544 | 50.0 | 1400 | 1.4968 | 0.2439 |
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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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