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smids_1x_deit_small_adamax_0001_fold3

Image Classification
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  2. model.safetensors +1 -1
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_1x_deit_small_adamax_0001_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: 0.9083333333333333
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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_small_adamax_0001_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: 0.5996
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+ - Accuracy: 0.9083
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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: 0.0001
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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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+ | 0.3937 | 1.0 | 75 | 0.3211 | 0.8633 |
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+ | 0.2826 | 2.0 | 150 | 0.3394 | 0.8733 |
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+ | 0.1652 | 3.0 | 225 | 0.2814 | 0.8967 |
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+ | 0.141 | 4.0 | 300 | 0.2984 | 0.9 |
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+ | 0.0291 | 5.0 | 375 | 0.3773 | 0.9017 |
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+ | 0.0153 | 6.0 | 450 | 0.4868 | 0.8733 |
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+ | 0.0215 | 7.0 | 525 | 0.4578 | 0.9067 |
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+ | 0.0108 | 8.0 | 600 | 0.5844 | 0.875 |
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+ | 0.0251 | 9.0 | 675 | 0.5045 | 0.9033 |
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+ | 0.0276 | 10.0 | 750 | 0.7184 | 0.875 |
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+ | 0.0071 | 11.0 | 825 | 0.5704 | 0.89 |
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+ | 0.0105 | 12.0 | 900 | 0.5523 | 0.9067 |
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+ | 0.0005 | 13.0 | 975 | 0.5683 | 0.9033 |
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+ | 0.0002 | 14.0 | 1050 | 0.5274 | 0.9033 |
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+ | 0.0001 | 15.0 | 1125 | 0.5432 | 0.895 |
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+ | 0.0001 | 16.0 | 1200 | 0.5742 | 0.8983 |
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+ | 0.0001 | 17.0 | 1275 | 0.5512 | 0.9067 |
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+ | 0.0071 | 18.0 | 1350 | 0.5548 | 0.9033 |
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+ | 0.0043 | 19.0 | 1425 | 0.5622 | 0.9083 |
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+ | 0.0063 | 20.0 | 1500 | 0.5939 | 0.9033 |
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+ | 0.0 | 21.0 | 1575 | 0.5379 | 0.9133 |
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+ | 0.0 | 22.0 | 1650 | 0.5428 | 0.91 |
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+ | 0.0037 | 23.0 | 1725 | 0.5469 | 0.9067 |
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+ | 0.0 | 24.0 | 1800 | 0.5517 | 0.9083 |
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+ | 0.0 | 25.0 | 1875 | 0.5493 | 0.9083 |
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+ | 0.0032 | 26.0 | 1950 | 0.5544 | 0.915 |
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+ | 0.0 | 27.0 | 2025 | 0.5586 | 0.9117 |
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+ | 0.0 | 28.0 | 2100 | 0.5623 | 0.9117 |
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+ | 0.0 | 29.0 | 2175 | 0.5631 | 0.9117 |
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+ | 0.0 | 30.0 | 2250 | 0.5628 | 0.91 |
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+ | 0.0 | 31.0 | 2325 | 0.5710 | 0.9117 |
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+ | 0.0 | 32.0 | 2400 | 0.5769 | 0.91 |
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+ | 0.0 | 33.0 | 2475 | 0.5763 | 0.91 |
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+ | 0.0048 | 34.0 | 2550 | 0.5811 | 0.9117 |
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+ | 0.0073 | 35.0 | 2625 | 0.5738 | 0.9117 |
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+ | 0.0031 | 36.0 | 2700 | 0.5751 | 0.91 |
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+ | 0.0023 | 37.0 | 2775 | 0.5897 | 0.9133 |
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+ | 0.0 | 38.0 | 2850 | 0.5810 | 0.91 |
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+ | 0.0 | 39.0 | 2925 | 0.5835 | 0.91 |
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+ | 0.0 | 40.0 | 3000 | 0.5848 | 0.9083 |
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+ | 0.0 | 41.0 | 3075 | 0.5898 | 0.91 |
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+ | 0.0027 | 42.0 | 3150 | 0.5994 | 0.915 |
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+ | 0.0028 | 43.0 | 3225 | 0.5922 | 0.91 |
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+ | 0.0 | 44.0 | 3300 | 0.5948 | 0.91 |
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+ | 0.0027 | 45.0 | 3375 | 0.5970 | 0.91 |
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+ | 0.0025 | 46.0 | 3450 | 0.5958 | 0.9083 |
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+ | 0.0051 | 47.0 | 3525 | 0.6023 | 0.9133 |
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+ | 0.0 | 48.0 | 3600 | 0.5981 | 0.9083 |
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+ | 0.0 | 49.0 | 3675 | 0.6006 | 0.91 |
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+ | 0.0045 | 50.0 | 3750 | 0.5996 | 0.9083 |
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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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