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smids_1x_deit_tiny_sgd_00001_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-tiny-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: smids_1x_deit_tiny_sgd_00001_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: 0.38333333333333336
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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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+ # smids_1x_deit_tiny_sgd_00001_fold3
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+
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+ This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2082
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+ - Accuracy: 0.3833
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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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+ | 1.3468 | 1.0 | 75 | 1.3684 | 0.345 |
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+ | 1.2941 | 2.0 | 150 | 1.3595 | 0.3433 |
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+ | 1.2835 | 3.0 | 225 | 1.3508 | 0.3433 |
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+ | 1.3718 | 4.0 | 300 | 1.3426 | 0.345 |
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+ | 1.2334 | 5.0 | 375 | 1.3348 | 0.345 |
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+ | 1.2846 | 6.0 | 450 | 1.3274 | 0.3467 |
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+ | 1.2876 | 7.0 | 525 | 1.3202 | 0.3483 |
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+ | 1.2894 | 8.0 | 600 | 1.3134 | 0.3483 |
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+ | 1.3322 | 9.0 | 675 | 1.3070 | 0.3483 |
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+ | 1.3642 | 10.0 | 750 | 1.3007 | 0.35 |
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+ | 1.2885 | 11.0 | 825 | 1.2947 | 0.3517 |
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+ | 1.2098 | 12.0 | 900 | 1.2891 | 0.3517 |
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+ | 1.2493 | 13.0 | 975 | 1.2838 | 0.35 |
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+ | 1.2305 | 14.0 | 1050 | 1.2787 | 0.3517 |
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+ | 1.2559 | 15.0 | 1125 | 1.2739 | 0.355 |
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+ | 1.216 | 16.0 | 1200 | 1.2692 | 0.3567 |
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+ | 1.2252 | 17.0 | 1275 | 1.2648 | 0.3583 |
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+ | 1.2555 | 18.0 | 1350 | 1.2606 | 0.36 |
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+ | 1.207 | 19.0 | 1425 | 1.2567 | 0.3583 |
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+ | 1.163 | 20.0 | 1500 | 1.2528 | 0.3583 |
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+ | 1.2799 | 21.0 | 1575 | 1.2493 | 0.3617 |
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+ | 1.2576 | 22.0 | 1650 | 1.2460 | 0.3633 |
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+ | 1.259 | 23.0 | 1725 | 1.2428 | 0.3617 |
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+ | 1.2102 | 24.0 | 1800 | 1.2399 | 0.365 |
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+ | 1.206 | 25.0 | 1875 | 1.2370 | 0.3633 |
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+ | 1.2525 | 26.0 | 1950 | 1.2343 | 0.3683 |
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+ | 1.2063 | 27.0 | 2025 | 1.2318 | 0.3683 |
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+ | 1.2191 | 28.0 | 2100 | 1.2294 | 0.3683 |
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+ | 1.2117 | 29.0 | 2175 | 1.2273 | 0.3683 |
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+ | 1.2241 | 30.0 | 2250 | 1.2252 | 0.37 |
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+ | 1.2256 | 31.0 | 2325 | 1.2233 | 0.3733 |
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+ | 1.123 | 32.0 | 2400 | 1.2215 | 0.3767 |
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+ | 1.1778 | 33.0 | 2475 | 1.2198 | 0.3767 |
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+ | 1.2098 | 34.0 | 2550 | 1.2183 | 0.3817 |
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+ | 1.1496 | 35.0 | 2625 | 1.2169 | 0.3783 |
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+ | 1.2108 | 36.0 | 2700 | 1.2156 | 0.3833 |
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+ | 1.2173 | 37.0 | 2775 | 1.2145 | 0.3817 |
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+ | 1.177 | 38.0 | 2850 | 1.2134 | 0.38 |
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+ | 1.1989 | 39.0 | 2925 | 1.2125 | 0.3783 |
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+ | 1.2161 | 40.0 | 3000 | 1.2116 | 0.3783 |
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+ | 1.2506 | 41.0 | 3075 | 1.2109 | 0.3783 |
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+ | 1.2753 | 42.0 | 3150 | 1.2102 | 0.38 |
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+ | 1.215 | 43.0 | 3225 | 1.2097 | 0.38 |
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+ | 1.196 | 44.0 | 3300 | 1.2092 | 0.38 |
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+ | 1.1971 | 45.0 | 3375 | 1.2089 | 0.3817 |
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+ | 1.1869 | 46.0 | 3450 | 1.2086 | 0.3833 |
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+ | 1.1695 | 47.0 | 3525 | 1.2084 | 0.3833 |
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+ | 1.19 | 48.0 | 3600 | 1.2083 | 0.3833 |
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+ | 1.1265 | 49.0 | 3675 | 1.2082 | 0.3833 |
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+ | 1.1801 | 50.0 | 3750 | 1.2082 | 0.3833 |
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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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