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smids_10x_deit_tiny_adamax_00001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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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_10x_deit_tiny_adamax_00001_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: 0.8935108153078203
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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_tiny_adamax_00001_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: 0.9769
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+ - Accuracy: 0.8935
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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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+ | 0.2848 | 1.0 | 750 | 0.3201 | 0.8702 |
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+ | 0.1766 | 2.0 | 1500 | 0.2882 | 0.8869 |
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+ | 0.1715 | 3.0 | 2250 | 0.2793 | 0.8918 |
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+ | 0.1528 | 4.0 | 3000 | 0.2928 | 0.8918 |
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+ | 0.1474 | 5.0 | 3750 | 0.3209 | 0.8902 |
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+ | 0.0904 | 6.0 | 4500 | 0.3386 | 0.8835 |
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+ | 0.0506 | 7.0 | 5250 | 0.4033 | 0.8918 |
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+ | 0.0528 | 8.0 | 6000 | 0.4656 | 0.8985 |
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+ | 0.0576 | 9.0 | 6750 | 0.4872 | 0.8852 |
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+ | 0.0247 | 10.0 | 7500 | 0.5636 | 0.8968 |
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+ | 0.0283 | 11.0 | 8250 | 0.5957 | 0.8918 |
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+ | 0.017 | 12.0 | 9000 | 0.6715 | 0.8952 |
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+ | 0.0013 | 13.0 | 9750 | 0.7105 | 0.8885 |
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+ | 0.0004 | 14.0 | 10500 | 0.7432 | 0.8819 |
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+ | 0.0011 | 15.0 | 11250 | 0.7702 | 0.8968 |
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+ | 0.0001 | 16.0 | 12000 | 0.8390 | 0.8935 |
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+ | 0.0001 | 17.0 | 12750 | 0.8598 | 0.8918 |
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+ | 0.0002 | 18.0 | 13500 | 0.8606 | 0.8852 |
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+ | 0.0 | 19.0 | 14250 | 0.8893 | 0.8902 |
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+ | 0.0001 | 20.0 | 15000 | 0.9092 | 0.8869 |
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+ | 0.0 | 21.0 | 15750 | 0.9243 | 0.8935 |
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+ | 0.0414 | 22.0 | 16500 | 0.8856 | 0.8935 |
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+ | 0.0196 | 23.0 | 17250 | 0.9361 | 0.8902 |
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+ | 0.0 | 24.0 | 18000 | 0.9456 | 0.8952 |
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+ | 0.0 | 25.0 | 18750 | 0.9417 | 0.8935 |
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+ | 0.0 | 26.0 | 19500 | 0.9178 | 0.8968 |
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+ | 0.0233 | 27.0 | 20250 | 0.9491 | 0.8902 |
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+ | 0.0 | 28.0 | 21000 | 0.9447 | 0.9002 |
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+ | 0.0 | 29.0 | 21750 | 0.9458 | 0.8952 |
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+ | 0.0 | 30.0 | 22500 | 0.9429 | 0.8935 |
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+ | 0.0 | 31.0 | 23250 | 0.9485 | 0.8902 |
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+ | 0.0 | 32.0 | 24000 | 0.9592 | 0.8918 |
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+ | 0.0 | 33.0 | 24750 | 0.9720 | 0.8935 |
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+ | 0.0 | 34.0 | 25500 | 0.9605 | 0.8918 |
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+ | 0.0 | 35.0 | 26250 | 0.9711 | 0.8952 |
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+ | 0.0 | 36.0 | 27000 | 0.9779 | 0.8902 |
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+ | 0.0248 | 37.0 | 27750 | 0.9824 | 0.8918 |
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+ | 0.0 | 38.0 | 28500 | 0.9776 | 0.8968 |
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+ | 0.0 | 39.0 | 29250 | 0.9729 | 0.8968 |
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+ | 0.0 | 40.0 | 30000 | 0.9687 | 0.8952 |
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+ | 0.0 | 41.0 | 30750 | 0.9781 | 0.8952 |
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+ | 0.0 | 42.0 | 31500 | 0.9860 | 0.8918 |
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+ | 0.0 | 43.0 | 32250 | 0.9836 | 0.8918 |
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+ | 0.0 | 44.0 | 33000 | 0.9765 | 0.8952 |
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+ | 0.0004 | 45.0 | 33750 | 0.9803 | 0.8935 |
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+ | 0.0 | 46.0 | 34500 | 0.9754 | 0.8952 |
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+ | 0.0 | 47.0 | 35250 | 0.9749 | 0.8952 |
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+ | 0.0 | 48.0 | 36000 | 0.9757 | 0.8952 |
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+ | 0.0 | 49.0 | 36750 | 0.9765 | 0.8935 |
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+ | 0.0 | 50.0 | 37500 | 0.9769 | 0.8935 |
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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 2.1.1+cu121
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.2
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