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hushem_1x_deit_tiny_rms_lr001_fold2

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: hushem_1x_deit_tiny_rms_lr001_fold2
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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: 0.35555555555555557
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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_tiny_rms_lr001_fold2
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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: 1.2600
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+ - Accuracy: 0.3556
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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 | 2.0874 | 0.2444 |
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+ | 4.6196 | 2.0 | 12 | 2.3422 | 0.2444 |
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+ | 4.6196 | 3.0 | 18 | 1.7914 | 0.2444 |
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+ | 1.8086 | 4.0 | 24 | 1.6082 | 0.2667 |
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+ | 1.5901 | 5.0 | 30 | 1.5144 | 0.2444 |
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+ | 1.5901 | 6.0 | 36 | 1.6190 | 0.2444 |
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+ | 1.5211 | 7.0 | 42 | 1.5231 | 0.2444 |
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+ | 1.5211 | 8.0 | 48 | 1.5027 | 0.2444 |
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+ | 1.4477 | 9.0 | 54 | 1.4266 | 0.2444 |
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+ | 1.4394 | 10.0 | 60 | 1.4345 | 0.2444 |
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+ | 1.4394 | 11.0 | 66 | 1.3152 | 0.4444 |
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+ | 1.3604 | 12.0 | 72 | 1.3376 | 0.2444 |
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+ | 1.3604 | 13.0 | 78 | 1.3260 | 0.2667 |
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+ | 1.3864 | 14.0 | 84 | 1.5120 | 0.2444 |
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+ | 1.3555 | 15.0 | 90 | 1.2685 | 0.3556 |
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+ | 1.3555 | 16.0 | 96 | 1.1751 | 0.4444 |
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+ | 1.3501 | 17.0 | 102 | 1.2648 | 0.4444 |
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+ | 1.3501 | 18.0 | 108 | 1.3992 | 0.3778 |
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+ | 1.2496 | 19.0 | 114 | 1.4208 | 0.2889 |
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+ | 1.2587 | 20.0 | 120 | 1.1782 | 0.4444 |
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+ | 1.2587 | 21.0 | 126 | 1.2882 | 0.4444 |
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+ | 1.2321 | 22.0 | 132 | 1.3142 | 0.4444 |
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+ | 1.2321 | 23.0 | 138 | 1.1784 | 0.4222 |
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+ | 1.1985 | 24.0 | 144 | 1.2247 | 0.3778 |
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+ | 1.234 | 25.0 | 150 | 1.2329 | 0.3778 |
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+ | 1.234 | 26.0 | 156 | 1.2482 | 0.3556 |
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+ | 1.1913 | 27.0 | 162 | 1.4153 | 0.3111 |
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+ | 1.1913 | 28.0 | 168 | 1.2994 | 0.3333 |
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+ | 1.1911 | 29.0 | 174 | 1.1400 | 0.4667 |
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+ | 1.1955 | 30.0 | 180 | 1.2156 | 0.3778 |
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+ | 1.1955 | 31.0 | 186 | 1.2232 | 0.4 |
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+ | 1.1751 | 32.0 | 192 | 1.3853 | 0.2889 |
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+ | 1.1751 | 33.0 | 198 | 1.2309 | 0.3333 |
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+ | 1.1328 | 34.0 | 204 | 1.2338 | 0.3333 |
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+ | 1.195 | 35.0 | 210 | 1.2383 | 0.3333 |
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+ | 1.195 | 36.0 | 216 | 1.2991 | 0.3778 |
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+ | 1.1661 | 37.0 | 222 | 1.3228 | 0.3556 |
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+ | 1.1661 | 38.0 | 228 | 1.2550 | 0.3778 |
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+ | 1.0748 | 39.0 | 234 | 1.2591 | 0.3556 |
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+ | 1.1122 | 40.0 | 240 | 1.2234 | 0.3778 |
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+ | 1.1122 | 41.0 | 246 | 1.2608 | 0.3556 |
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+ | 1.102 | 42.0 | 252 | 1.2600 | 0.3556 |
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+ | 1.102 | 43.0 | 258 | 1.2600 | 0.3556 |
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+ | 1.0792 | 44.0 | 264 | 1.2600 | 0.3556 |
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+ | 1.1126 | 45.0 | 270 | 1.2600 | 0.3556 |
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+ | 1.1126 | 46.0 | 276 | 1.2600 | 0.3556 |
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+ | 1.0704 | 47.0 | 282 | 1.2600 | 0.3556 |
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+ | 1.0704 | 48.0 | 288 | 1.2600 | 0.3556 |
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+ | 1.1302 | 49.0 | 294 | 1.2600 | 0.3556 |
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+ | 1.0797 | 50.0 | 300 | 1.2600 | 0.3556 |
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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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