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smids_1x_beit_base_rms_00001_fold3

Image Classification
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beit
Generated from Trainer
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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: microsoft/beit-base-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_beit_base_rms_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: 0.92
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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_beit_base_rms_00001_fold3
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+
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6179
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+ - Accuracy: 0.92
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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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+ | 0.378 | 1.0 | 75 | 0.2655 | 0.905 |
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+ | 0.1995 | 2.0 | 150 | 0.2472 | 0.9067 |
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+ | 0.1269 | 3.0 | 225 | 0.2601 | 0.9133 |
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+ | 0.0982 | 4.0 | 300 | 0.2718 | 0.9183 |
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+ | 0.0334 | 5.0 | 375 | 0.3064 | 0.9183 |
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+ | 0.0325 | 6.0 | 450 | 0.3593 | 0.9017 |
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+ | 0.0122 | 7.0 | 525 | 0.4158 | 0.9133 |
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+ | 0.0276 | 8.0 | 600 | 0.3999 | 0.915 |
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+ | 0.0023 | 9.0 | 675 | 0.4376 | 0.91 |
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+ | 0.0029 | 10.0 | 750 | 0.4955 | 0.91 |
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+ | 0.0282 | 11.0 | 825 | 0.4886 | 0.9133 |
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+ | 0.0074 | 12.0 | 900 | 0.4903 | 0.9083 |
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+ | 0.0119 | 13.0 | 975 | 0.4968 | 0.9183 |
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+ | 0.0151 | 14.0 | 1050 | 0.4966 | 0.9067 |
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+ | 0.0139 | 15.0 | 1125 | 0.4573 | 0.9267 |
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+ | 0.0049 | 16.0 | 1200 | 0.4797 | 0.9267 |
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+ | 0.0357 | 17.0 | 1275 | 0.4808 | 0.9317 |
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+ | 0.0195 | 18.0 | 1350 | 0.5297 | 0.9133 |
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+ | 0.0164 | 19.0 | 1425 | 0.5446 | 0.9233 |
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+ | 0.0136 | 20.0 | 1500 | 0.5630 | 0.915 |
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+ | 0.0002 | 21.0 | 1575 | 0.6196 | 0.9083 |
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+ | 0.0053 | 22.0 | 1650 | 0.5529 | 0.915 |
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+ | 0.002 | 23.0 | 1725 | 0.5621 | 0.9183 |
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+ | 0.0001 | 24.0 | 1800 | 0.5333 | 0.9233 |
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+ | 0.0008 | 25.0 | 1875 | 0.5371 | 0.9217 |
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+ | 0.0014 | 26.0 | 1950 | 0.5172 | 0.93 |
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+ | 0.0001 | 27.0 | 2025 | 0.5437 | 0.9233 |
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+ | 0.0001 | 28.0 | 2100 | 0.5344 | 0.9283 |
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+ | 0.0001 | 29.0 | 2175 | 0.5536 | 0.9183 |
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+ | 0.0075 | 30.0 | 2250 | 0.6086 | 0.9083 |
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+ | 0.0046 | 31.0 | 2325 | 0.5570 | 0.9133 |
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+ | 0.0077 | 32.0 | 2400 | 0.6038 | 0.915 |
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+ | 0.0016 | 33.0 | 2475 | 0.6324 | 0.9133 |
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+ | 0.0004 | 34.0 | 2550 | 0.5847 | 0.9217 |
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+ | 0.0039 | 35.0 | 2625 | 0.6482 | 0.9183 |
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+ | 0.0029 | 36.0 | 2700 | 0.6146 | 0.9267 |
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+ | 0.0076 | 37.0 | 2775 | 0.5750 | 0.9217 |
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+ | 0.0017 | 38.0 | 2850 | 0.5846 | 0.9233 |
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+ | 0.0 | 39.0 | 2925 | 0.5952 | 0.9233 |
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+ | 0.0018 | 40.0 | 3000 | 0.6016 | 0.9217 |
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+ | 0.0 | 41.0 | 3075 | 0.6081 | 0.9267 |
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+ | 0.0026 | 42.0 | 3150 | 0.6036 | 0.9233 |
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+ | 0.0001 | 43.0 | 3225 | 0.6419 | 0.915 |
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+ | 0.0 | 44.0 | 3300 | 0.6346 | 0.915 |
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+ | 0.0 | 45.0 | 3375 | 0.6400 | 0.915 |
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+ | 0.0001 | 46.0 | 3450 | 0.6220 | 0.9233 |
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+ | 0.0039 | 47.0 | 3525 | 0.6179 | 0.9233 |
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+ | 0.0001 | 48.0 | 3600 | 0.6159 | 0.9183 |
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+ | 0.0 | 49.0 | 3675 | 0.6170 | 0.92 |
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+ | 0.0 | 50.0 | 3750 | 0.6179 | 0.92 |
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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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