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smids_10x_deit_small_adamax_00001_fold3

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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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: smids_10x_deit_small_adamax_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: 0.9183333333333333
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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_10x_deit_small_adamax_00001_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: 0.8187
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+ - Accuracy: 0.9183
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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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+ | 0.2585 | 1.0 | 750 | 0.2681 | 0.8983 |
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+ | 0.2105 | 2.0 | 1500 | 0.2490 | 0.9167 |
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+ | 0.0786 | 3.0 | 2250 | 0.2625 | 0.9167 |
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+ | 0.0736 | 4.0 | 3000 | 0.2826 | 0.9133 |
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+ | 0.0688 | 5.0 | 3750 | 0.3568 | 0.91 |
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+ | 0.0468 | 6.0 | 4500 | 0.4349 | 0.9083 |
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+ | 0.0289 | 7.0 | 5250 | 0.4645 | 0.9183 |
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+ | 0.0394 | 8.0 | 6000 | 0.5300 | 0.9183 |
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+ | 0.0012 | 9.0 | 6750 | 0.5842 | 0.92 |
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+ | 0.0139 | 10.0 | 7500 | 0.6285 | 0.915 |
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+ | 0.0002 | 11.0 | 8250 | 0.6464 | 0.9217 |
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+ | 0.0001 | 12.0 | 9000 | 0.6757 | 0.9133 |
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+ | 0.0 | 13.0 | 9750 | 0.7480 | 0.9167 |
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+ | 0.0001 | 14.0 | 10500 | 0.7033 | 0.92 |
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+ | 0.0 | 15.0 | 11250 | 0.7525 | 0.9133 |
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+ | 0.0 | 16.0 | 12000 | 0.7472 | 0.915 |
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+ | 0.0 | 17.0 | 12750 | 0.7380 | 0.92 |
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+ | 0.0 | 18.0 | 13500 | 0.7432 | 0.9183 |
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+ | 0.0 | 19.0 | 14250 | 0.7438 | 0.9217 |
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+ | 0.0 | 20.0 | 15000 | 0.7615 | 0.92 |
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+ | 0.0 | 21.0 | 15750 | 0.7581 | 0.9233 |
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+ | 0.0 | 22.0 | 16500 | 0.7753 | 0.92 |
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+ | 0.0 | 23.0 | 17250 | 0.7758 | 0.92 |
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+ | 0.0 | 24.0 | 18000 | 0.7745 | 0.9217 |
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+ | 0.0 | 25.0 | 18750 | 0.7780 | 0.9233 |
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+ | 0.0 | 26.0 | 19500 | 0.7763 | 0.9217 |
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+ | 0.0 | 27.0 | 20250 | 0.7839 | 0.9183 |
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+ | 0.0 | 28.0 | 21000 | 0.7914 | 0.9183 |
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+ | 0.0 | 29.0 | 21750 | 0.7935 | 0.92 |
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+ | 0.0 | 30.0 | 22500 | 0.8320 | 0.9117 |
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+ | 0.0 | 31.0 | 23250 | 0.8021 | 0.9183 |
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+ | 0.0 | 32.0 | 24000 | 0.8041 | 0.9217 |
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+ | 0.0 | 33.0 | 24750 | 0.8030 | 0.9167 |
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+ | 0.0 | 34.0 | 25500 | 0.8170 | 0.9133 |
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+ | 0.0 | 35.0 | 26250 | 0.8237 | 0.915 |
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+ | 0.0 | 36.0 | 27000 | 0.8072 | 0.9167 |
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+ | 0.0 | 37.0 | 27750 | 0.8249 | 0.915 |
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+ | 0.0 | 38.0 | 28500 | 0.8116 | 0.9167 |
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+ | 0.0 | 39.0 | 29250 | 0.8160 | 0.9217 |
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+ | 0.0 | 40.0 | 30000 | 0.8158 | 0.92 |
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+ | 0.0 | 41.0 | 30750 | 0.8164 | 0.92 |
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+ | 0.0 | 42.0 | 31500 | 0.8163 | 0.92 |
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+ | 0.0 | 43.0 | 32250 | 0.8169 | 0.92 |
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+ | 0.0 | 44.0 | 33000 | 0.8174 | 0.92 |
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+ | 0.0 | 45.0 | 33750 | 0.8182 | 0.92 |
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+ | 0.0 | 46.0 | 34500 | 0.8186 | 0.9183 |
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+ | 0.0 | 47.0 | 35250 | 0.8185 | 0.92 |
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+ | 0.0 | 48.0 | 36000 | 0.8187 | 0.92 |
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+ | 0.0 | 49.0 | 36750 | 0.8181 | 0.9183 |
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+ | 0.0 | 50.0 | 37500 | 0.8187 | 0.9183 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.1
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.2
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