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smids_1x_beit_base_adamax_0001_fold2

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: 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_adamax_0001_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: 0.8901830282861897
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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_adamax_0001_fold2
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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: 0.7859
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+ - Accuracy: 0.8902
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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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+ | 0.357 | 1.0 | 75 | 0.2942 | 0.8852 |
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+ | 0.196 | 2.0 | 150 | 0.2977 | 0.8769 |
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+ | 0.1343 | 3.0 | 225 | 0.3454 | 0.8835 |
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+ | 0.1165 | 4.0 | 300 | 0.4770 | 0.8586 |
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+ | 0.0357 | 5.0 | 375 | 0.3863 | 0.8819 |
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+ | 0.0407 | 6.0 | 450 | 0.5588 | 0.8785 |
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+ | 0.0487 | 7.0 | 525 | 0.5410 | 0.8769 |
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+ | 0.0422 | 8.0 | 600 | 0.5327 | 0.8835 |
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+ | 0.0252 | 9.0 | 675 | 0.5671 | 0.8885 |
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+ | 0.0072 | 10.0 | 750 | 0.5229 | 0.8852 |
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+ | 0.0013 | 11.0 | 825 | 0.5397 | 0.9018 |
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+ | 0.0233 | 12.0 | 900 | 0.6716 | 0.8902 |
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+ | 0.0031 | 13.0 | 975 | 0.6232 | 0.8935 |
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+ | 0.0106 | 14.0 | 1050 | 0.6722 | 0.8835 |
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+ | 0.0052 | 15.0 | 1125 | 0.5873 | 0.9101 |
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+ | 0.0117 | 16.0 | 1200 | 0.6014 | 0.8935 |
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+ | 0.0056 | 17.0 | 1275 | 0.6190 | 0.8952 |
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+ | 0.018 | 18.0 | 1350 | 0.6714 | 0.8902 |
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+ | 0.0034 | 19.0 | 1425 | 0.6903 | 0.8918 |
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+ | 0.0034 | 20.0 | 1500 | 0.6789 | 0.8902 |
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+ | 0.0018 | 21.0 | 1575 | 0.7049 | 0.8852 |
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+ | 0.0015 | 22.0 | 1650 | 0.8451 | 0.8802 |
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+ | 0.0032 | 23.0 | 1725 | 0.6725 | 0.8885 |
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+ | 0.0116 | 24.0 | 1800 | 0.7163 | 0.8952 |
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+ | 0.0001 | 25.0 | 1875 | 0.6827 | 0.8918 |
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+ | 0.004 | 26.0 | 1950 | 0.7084 | 0.8885 |
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+ | 0.012 | 27.0 | 2025 | 0.7239 | 0.8968 |
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+ | 0.0099 | 28.0 | 2100 | 0.7371 | 0.8918 |
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+ | 0.0044 | 29.0 | 2175 | 0.7635 | 0.8869 |
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+ | 0.0039 | 30.0 | 2250 | 0.7043 | 0.8918 |
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+ | 0.0035 | 31.0 | 2325 | 0.7276 | 0.8902 |
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+ | 0.0 | 32.0 | 2400 | 0.7428 | 0.8935 |
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+ | 0.0 | 33.0 | 2475 | 0.7968 | 0.8852 |
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+ | 0.014 | 34.0 | 2550 | 0.7553 | 0.8918 |
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+ | 0.0048 | 35.0 | 2625 | 0.7230 | 0.8968 |
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+ | 0.0029 | 36.0 | 2700 | 0.7674 | 0.8869 |
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+ | 0.0 | 37.0 | 2775 | 0.7425 | 0.8918 |
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+ | 0.0023 | 38.0 | 2850 | 0.7970 | 0.8902 |
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+ | 0.0047 | 39.0 | 2925 | 0.8047 | 0.8869 |
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+ | 0.0021 | 40.0 | 3000 | 0.7994 | 0.8885 |
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+ | 0.0 | 41.0 | 3075 | 0.7761 | 0.8852 |
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+ | 0.0025 | 42.0 | 3150 | 0.7890 | 0.8885 |
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+ | 0.0046 | 43.0 | 3225 | 0.7889 | 0.8885 |
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+ | 0.0 | 44.0 | 3300 | 0.7915 | 0.8852 |
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+ | 0.0047 | 45.0 | 3375 | 0.7967 | 0.8885 |
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+ | 0.0 | 46.0 | 3450 | 0.7946 | 0.8869 |
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+ | 0.002 | 47.0 | 3525 | 0.7884 | 0.8885 |
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+ | 0.0 | 48.0 | 3600 | 0.7873 | 0.8885 |
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+ | 0.0 | 49.0 | 3675 | 0.7859 | 0.8902 |
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+ | 0.0 | 50.0 | 3750 | 0.7859 | 0.8902 |
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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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