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smids_1x_deit_small_sgd_001_fold3

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-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_sgd_001_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: 0.8716666666666667
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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_sgd_001_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: 0.3620
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+ - Accuracy: 0.8717
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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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+ | 0.9897 | 1.0 | 75 | 0.9617 | 0.5517 |
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+ | 0.8723 | 2.0 | 150 | 0.8626 | 0.625 |
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+ | 0.7649 | 3.0 | 225 | 0.7773 | 0.7033 |
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+ | 0.6947 | 4.0 | 300 | 0.7095 | 0.7417 |
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+ | 0.6117 | 5.0 | 375 | 0.6569 | 0.7517 |
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+ | 0.5694 | 6.0 | 450 | 0.6132 | 0.7833 |
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+ | 0.5545 | 7.0 | 525 | 0.5789 | 0.79 |
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+ | 0.4978 | 8.0 | 600 | 0.5508 | 0.7967 |
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+ | 0.5086 | 9.0 | 675 | 0.5269 | 0.8017 |
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+ | 0.4854 | 10.0 | 750 | 0.5107 | 0.8033 |
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+ | 0.442 | 11.0 | 825 | 0.4925 | 0.815 |
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+ | 0.4253 | 12.0 | 900 | 0.4789 | 0.8217 |
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+ | 0.4589 | 13.0 | 975 | 0.4669 | 0.8217 |
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+ | 0.402 | 14.0 | 1050 | 0.4553 | 0.82 |
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+ | 0.3349 | 15.0 | 1125 | 0.4468 | 0.8283 |
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+ | 0.3869 | 16.0 | 1200 | 0.4398 | 0.8333 |
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+ | 0.3789 | 17.0 | 1275 | 0.4312 | 0.8367 |
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+ | 0.3564 | 18.0 | 1350 | 0.4255 | 0.84 |
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+ | 0.3321 | 19.0 | 1425 | 0.4198 | 0.84 |
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+ | 0.3788 | 20.0 | 1500 | 0.4135 | 0.8383 |
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+ | 0.3599 | 21.0 | 1575 | 0.4108 | 0.8417 |
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+ | 0.3259 | 22.0 | 1650 | 0.4045 | 0.8417 |
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+ | 0.3384 | 23.0 | 1725 | 0.4010 | 0.8433 |
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+ | 0.3143 | 24.0 | 1800 | 0.3966 | 0.8433 |
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+ | 0.3495 | 25.0 | 1875 | 0.3938 | 0.8483 |
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+ | 0.3642 | 26.0 | 1950 | 0.3902 | 0.8517 |
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+ | 0.2826 | 27.0 | 2025 | 0.3879 | 0.855 |
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+ | 0.3052 | 28.0 | 2100 | 0.3848 | 0.8533 |
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+ | 0.3344 | 29.0 | 2175 | 0.3828 | 0.855 |
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+ | 0.3229 | 30.0 | 2250 | 0.3809 | 0.8533 |
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+ | 0.3173 | 31.0 | 2325 | 0.3785 | 0.8567 |
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+ | 0.3012 | 32.0 | 2400 | 0.3761 | 0.8567 |
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+ | 0.2954 | 33.0 | 2475 | 0.3749 | 0.8617 |
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+ | 0.2924 | 34.0 | 2550 | 0.3731 | 0.8633 |
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+ | 0.3077 | 35.0 | 2625 | 0.3719 | 0.8667 |
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+ | 0.3047 | 36.0 | 2700 | 0.3705 | 0.8667 |
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+ | 0.2425 | 37.0 | 2775 | 0.3691 | 0.8683 |
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+ | 0.3384 | 38.0 | 2850 | 0.3680 | 0.8683 |
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+ | 0.2795 | 39.0 | 2925 | 0.3666 | 0.8683 |
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+ | 0.2754 | 40.0 | 3000 | 0.3660 | 0.87 |
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+ | 0.2793 | 41.0 | 3075 | 0.3650 | 0.8683 |
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+ | 0.288 | 42.0 | 3150 | 0.3645 | 0.87 |
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+ | 0.3153 | 43.0 | 3225 | 0.3639 | 0.87 |
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+ | 0.2599 | 44.0 | 3300 | 0.3636 | 0.8717 |
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+ | 0.3229 | 45.0 | 3375 | 0.3630 | 0.8717 |
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+ | 0.297 | 46.0 | 3450 | 0.3626 | 0.8717 |
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+ | 0.2632 | 47.0 | 3525 | 0.3624 | 0.8717 |
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+ | 0.3026 | 48.0 | 3600 | 0.3623 | 0.8717 |
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+ | 0.3009 | 49.0 | 3675 | 0.3621 | 0.8717 |
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+ | 0.2576 | 50.0 | 3750 | 0.3620 | 0.8717 |
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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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