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smids_3x_deit_tiny_adamax_00001_fold2

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-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_3x_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: 0.8718801996672213
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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_3x_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: 0.9651
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+ - Accuracy: 0.8719
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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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+ | 0.4234 | 1.0 | 225 | 0.4415 | 0.8186 |
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+ | 0.3257 | 2.0 | 450 | 0.3946 | 0.8353 |
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+ | 0.2484 | 3.0 | 675 | 0.3209 | 0.8719 |
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+ | 0.2642 | 4.0 | 900 | 0.3296 | 0.8702 |
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+ | 0.1771 | 5.0 | 1125 | 0.3172 | 0.8702 |
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+ | 0.173 | 6.0 | 1350 | 0.3544 | 0.8586 |
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+ | 0.1484 | 7.0 | 1575 | 0.3447 | 0.8702 |
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+ | 0.0971 | 8.0 | 1800 | 0.3412 | 0.8785 |
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+ | 0.1373 | 9.0 | 2025 | 0.3898 | 0.8735 |
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+ | 0.0316 | 10.0 | 2250 | 0.4151 | 0.8702 |
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+ | 0.056 | 11.0 | 2475 | 0.4368 | 0.8669 |
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+ | 0.0751 | 12.0 | 2700 | 0.5052 | 0.8752 |
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+ | 0.0299 | 13.0 | 2925 | 0.5143 | 0.8769 |
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+ | 0.0139 | 14.0 | 3150 | 0.5498 | 0.8802 |
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+ | 0.0304 | 15.0 | 3375 | 0.6069 | 0.8752 |
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+ | 0.0339 | 16.0 | 3600 | 0.6246 | 0.8785 |
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+ | 0.0114 | 17.0 | 3825 | 0.6695 | 0.8735 |
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+ | 0.0005 | 18.0 | 4050 | 0.7207 | 0.8702 |
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+ | 0.0014 | 19.0 | 4275 | 0.7338 | 0.8669 |
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+ | 0.0073 | 20.0 | 4500 | 0.7686 | 0.8669 |
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+ | 0.0003 | 21.0 | 4725 | 0.8099 | 0.8669 |
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+ | 0.0003 | 22.0 | 4950 | 0.8291 | 0.8686 |
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+ | 0.002 | 23.0 | 5175 | 0.8321 | 0.8669 |
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+ | 0.0005 | 24.0 | 5400 | 0.8587 | 0.8669 |
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+ | 0.0136 | 25.0 | 5625 | 0.8652 | 0.8702 |
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+ | 0.0002 | 26.0 | 5850 | 0.8692 | 0.8702 |
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+ | 0.0001 | 27.0 | 6075 | 0.8870 | 0.8702 |
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+ | 0.0001 | 28.0 | 6300 | 0.8976 | 0.8686 |
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+ | 0.0057 | 29.0 | 6525 | 0.9057 | 0.8719 |
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+ | 0.0181 | 30.0 | 6750 | 0.9160 | 0.8719 |
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+ | 0.0001 | 31.0 | 6975 | 0.9098 | 0.8702 |
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+ | 0.0001 | 32.0 | 7200 | 0.9072 | 0.8719 |
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+ | 0.0001 | 33.0 | 7425 | 0.9201 | 0.8686 |
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+ | 0.0001 | 34.0 | 7650 | 0.9257 | 0.8752 |
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+ | 0.0 | 35.0 | 7875 | 0.9399 | 0.8702 |
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+ | 0.0059 | 36.0 | 8100 | 0.9356 | 0.8686 |
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+ | 0.0 | 37.0 | 8325 | 0.9495 | 0.8719 |
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+ | 0.0 | 38.0 | 8550 | 0.9431 | 0.8702 |
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+ | 0.0039 | 39.0 | 8775 | 0.9538 | 0.8702 |
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+ | 0.0 | 40.0 | 9000 | 0.9587 | 0.8686 |
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+ | 0.0 | 41.0 | 9225 | 0.9526 | 0.8702 |
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+ | 0.0 | 42.0 | 9450 | 0.9618 | 0.8686 |
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+ | 0.0 | 43.0 | 9675 | 0.9656 | 0.8719 |
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+ | 0.0 | 44.0 | 9900 | 0.9609 | 0.8719 |
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+ | 0.0001 | 45.0 | 10125 | 0.9585 | 0.8719 |
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+ | 0.0 | 46.0 | 10350 | 0.9641 | 0.8702 |
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+ | 0.0 | 47.0 | 10575 | 0.9675 | 0.8686 |
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+ | 0.0 | 48.0 | 10800 | 0.9660 | 0.8719 |
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+ | 0.0045 | 49.0 | 11025 | 0.9652 | 0.8719 |
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+ | 0.0044 | 50.0 | 11250 | 0.9651 | 0.8719 |
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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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