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smids_1x_beit_base_adamax_001_fold3

Image Classification
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  2. model.safetensors +1 -1
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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_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: 0.8066666666666666
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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_001_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: 1.6207
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+ - Accuracy: 0.8067
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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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+ | 0.961 | 1.0 | 75 | 0.9579 | 0.5083 |
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+ | 0.8519 | 2.0 | 150 | 0.8223 | 0.555 |
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+ | 0.8429 | 3.0 | 225 | 0.8258 | 0.5417 |
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+ | 0.8689 | 4.0 | 300 | 1.1933 | 0.5183 |
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+ | 0.7212 | 5.0 | 375 | 0.6887 | 0.7133 |
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+ | 0.649 | 6.0 | 450 | 0.7128 | 0.6567 |
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+ | 0.6409 | 7.0 | 525 | 0.6763 | 0.71 |
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+ | 0.5869 | 8.0 | 600 | 0.5948 | 0.7383 |
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+ | 0.5565 | 9.0 | 675 | 0.6418 | 0.695 |
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+ | 0.5839 | 10.0 | 750 | 0.6087 | 0.7267 |
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+ | 0.5293 | 11.0 | 825 | 0.5977 | 0.7267 |
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+ | 0.4762 | 12.0 | 900 | 0.5491 | 0.7783 |
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+ | 0.4499 | 13.0 | 975 | 0.5838 | 0.7517 |
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+ | 0.4302 | 14.0 | 1050 | 0.5473 | 0.77 |
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+ | 0.4099 | 15.0 | 1125 | 0.5508 | 0.755 |
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+ | 0.3178 | 16.0 | 1200 | 0.5699 | 0.78 |
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+ | 0.341 | 17.0 | 1275 | 0.6033 | 0.7933 |
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+ | 0.2555 | 18.0 | 1350 | 0.6573 | 0.7767 |
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+ | 0.3366 | 19.0 | 1425 | 0.5611 | 0.7933 |
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+ | 0.1724 | 20.0 | 1500 | 0.7339 | 0.7933 |
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+ | 0.2297 | 21.0 | 1575 | 0.8132 | 0.78 |
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+ | 0.2293 | 22.0 | 1650 | 0.7112 | 0.7833 |
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+ | 0.1656 | 23.0 | 1725 | 0.8681 | 0.7767 |
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+ | 0.1488 | 24.0 | 1800 | 0.9454 | 0.79 |
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+ | 0.1667 | 25.0 | 1875 | 0.9934 | 0.7767 |
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+ | 0.0534 | 26.0 | 1950 | 0.9484 | 0.7767 |
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+ | 0.1635 | 27.0 | 2025 | 1.0833 | 0.77 |
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+ | 0.0554 | 28.0 | 2100 | 1.1552 | 0.8017 |
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+ | 0.0938 | 29.0 | 2175 | 1.0865 | 0.7917 |
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+ | 0.1141 | 30.0 | 2250 | 1.3605 | 0.7883 |
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+ | 0.0561 | 31.0 | 2325 | 1.2003 | 0.8033 |
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+ | 0.064 | 32.0 | 2400 | 1.3257 | 0.7933 |
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+ | 0.0695 | 33.0 | 2475 | 1.6036 | 0.7883 |
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+ | 0.0143 | 34.0 | 2550 | 1.5166 | 0.7717 |
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+ | 0.0099 | 35.0 | 2625 | 1.5177 | 0.7833 |
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+ | 0.046 | 36.0 | 2700 | 1.6809 | 0.7983 |
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+ | 0.0535 | 37.0 | 2775 | 1.6548 | 0.7783 |
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+ | 0.0142 | 38.0 | 2850 | 1.9052 | 0.7867 |
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+ | 0.0043 | 39.0 | 2925 | 1.8855 | 0.785 |
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+ | 0.0169 | 40.0 | 3000 | 1.8422 | 0.7983 |
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+ | 0.0085 | 41.0 | 3075 | 1.6803 | 0.8033 |
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+ | 0.0125 | 42.0 | 3150 | 1.4852 | 0.8033 |
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+ | 0.0037 | 43.0 | 3225 | 1.5490 | 0.7883 |
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+ | 0.0153 | 44.0 | 3300 | 1.3985 | 0.81 |
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+ | 0.0066 | 45.0 | 3375 | 1.5369 | 0.8083 |
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+ | 0.0076 | 46.0 | 3450 | 1.5177 | 0.7983 |
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+ | 0.0089 | 47.0 | 3525 | 1.6039 | 0.7883 |
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+ | 0.0027 | 48.0 | 3600 | 1.6013 | 0.8067 |
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+ | 0.0003 | 49.0 | 3675 | 1.6182 | 0.8067 |
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+ | 0.0026 | 50.0 | 3750 | 1.6207 | 0.8067 |
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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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