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hushem_1x_deit_small_sgd_001_fold3

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  2. model.safetensors +1 -1
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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_1x_deit_small_sgd_001_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: 0.2558139534883721
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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_1x_deit_small_sgd_001_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: 1.3255
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+ - Accuracy: 0.2558
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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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+ | No log | 1.0 | 6 | 1.5642 | 0.2326 |
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+ | 1.4806 | 2.0 | 12 | 1.5332 | 0.3256 |
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+ | 1.4806 | 3.0 | 18 | 1.5110 | 0.3256 |
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+ | 1.4127 | 4.0 | 24 | 1.4910 | 0.3256 |
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+ | 1.3859 | 5.0 | 30 | 1.4734 | 0.3256 |
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+ | 1.3859 | 6.0 | 36 | 1.4581 | 0.3256 |
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+ | 1.372 | 7.0 | 42 | 1.4448 | 0.3256 |
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+ | 1.372 | 8.0 | 48 | 1.4360 | 0.3256 |
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+ | 1.3407 | 9.0 | 54 | 1.4268 | 0.3256 |
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+ | 1.3476 | 10.0 | 60 | 1.4184 | 0.3256 |
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+ | 1.3476 | 11.0 | 66 | 1.4115 | 0.3256 |
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+ | 1.3176 | 12.0 | 72 | 1.4055 | 0.3488 |
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+ | 1.3176 | 13.0 | 78 | 1.3989 | 0.3488 |
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+ | 1.3009 | 14.0 | 84 | 1.3926 | 0.3256 |
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+ | 1.3032 | 15.0 | 90 | 1.3870 | 0.3256 |
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+ | 1.3032 | 16.0 | 96 | 1.3815 | 0.3256 |
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+ | 1.2893 | 17.0 | 102 | 1.3768 | 0.3256 |
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+ | 1.2893 | 18.0 | 108 | 1.3723 | 0.3023 |
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+ | 1.252 | 19.0 | 114 | 1.3680 | 0.3023 |
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+ | 1.2643 | 20.0 | 120 | 1.3638 | 0.3023 |
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+ | 1.2643 | 21.0 | 126 | 1.3601 | 0.2791 |
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+ | 1.2642 | 22.0 | 132 | 1.3567 | 0.2791 |
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+ | 1.2642 | 23.0 | 138 | 1.3535 | 0.2791 |
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+ | 1.2369 | 24.0 | 144 | 1.3502 | 0.2791 |
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+ | 1.2315 | 25.0 | 150 | 1.3476 | 0.2791 |
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+ | 1.2315 | 26.0 | 156 | 1.3450 | 0.2791 |
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+ | 1.2236 | 27.0 | 162 | 1.3424 | 0.2558 |
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+ | 1.2236 | 28.0 | 168 | 1.3403 | 0.2558 |
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+ | 1.2327 | 29.0 | 174 | 1.3382 | 0.2558 |
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+ | 1.2254 | 30.0 | 180 | 1.3363 | 0.2558 |
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+ | 1.2254 | 31.0 | 186 | 1.3347 | 0.2558 |
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+ | 1.2165 | 32.0 | 192 | 1.3331 | 0.2558 |
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+ | 1.2165 | 33.0 | 198 | 1.3315 | 0.2558 |
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+ | 1.2003 | 34.0 | 204 | 1.3303 | 0.2558 |
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+ | 1.2034 | 35.0 | 210 | 1.3292 | 0.2558 |
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+ | 1.2034 | 36.0 | 216 | 1.3282 | 0.2558 |
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+ | 1.2052 | 37.0 | 222 | 1.3273 | 0.2558 |
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+ | 1.2052 | 38.0 | 228 | 1.3266 | 0.2558 |
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+ | 1.2216 | 39.0 | 234 | 1.3261 | 0.2558 |
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+ | 1.2003 | 40.0 | 240 | 1.3258 | 0.2558 |
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+ | 1.2003 | 41.0 | 246 | 1.3256 | 0.2558 |
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+ | 1.1856 | 42.0 | 252 | 1.3255 | 0.2558 |
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+ | 1.1856 | 43.0 | 258 | 1.3255 | 0.2558 |
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+ | 1.2091 | 44.0 | 264 | 1.3255 | 0.2558 |
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+ | 1.1987 | 45.0 | 270 | 1.3255 | 0.2558 |
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+ | 1.1987 | 46.0 | 276 | 1.3255 | 0.2558 |
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+ | 1.1885 | 47.0 | 282 | 1.3255 | 0.2558 |
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+ | 1.1885 | 48.0 | 288 | 1.3255 | 0.2558 |
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+ | 1.2076 | 49.0 | 294 | 1.3255 | 0.2558 |
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+ | 1.2139 | 50.0 | 300 | 1.3255 | 0.2558 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.0
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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