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smids_3x_beit_base_rms_00001_fold2

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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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_3x_beit_base_rms_00001_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: 0.9001663893510815
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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_3x_beit_base_rms_00001_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: 0.9909
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+ - Accuracy: 0.9002
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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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+ | 0.2362 | 1.0 | 225 | 0.2699 | 0.9035 |
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+ | 0.1679 | 2.0 | 450 | 0.2716 | 0.9101 |
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+ | 0.0726 | 3.0 | 675 | 0.3178 | 0.8968 |
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+ | 0.0695 | 4.0 | 900 | 0.3843 | 0.9035 |
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+ | 0.0417 | 5.0 | 1125 | 0.4680 | 0.8835 |
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+ | 0.0473 | 6.0 | 1350 | 0.5055 | 0.9002 |
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+ | 0.0543 | 7.0 | 1575 | 0.5652 | 0.8935 |
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+ | 0.0498 | 8.0 | 1800 | 0.5730 | 0.9018 |
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+ | 0.0051 | 9.0 | 2025 | 0.7436 | 0.8985 |
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+ | 0.0809 | 10.0 | 2250 | 0.7929 | 0.8968 |
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+ | 0.0184 | 11.0 | 2475 | 0.8279 | 0.8702 |
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+ | 0.0005 | 12.0 | 2700 | 0.7228 | 0.8985 |
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+ | 0.0232 | 13.0 | 2925 | 0.7729 | 0.8869 |
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+ | 0.0419 | 14.0 | 3150 | 0.7425 | 0.8985 |
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+ | 0.0106 | 15.0 | 3375 | 0.7246 | 0.8952 |
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+ | 0.0338 | 16.0 | 3600 | 0.7871 | 0.8885 |
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+ | 0.0349 | 17.0 | 3825 | 0.8649 | 0.9002 |
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+ | 0.0092 | 18.0 | 4050 | 0.7633 | 0.8902 |
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+ | 0.0509 | 19.0 | 4275 | 0.8796 | 0.8885 |
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+ | 0.0566 | 20.0 | 4500 | 0.8230 | 0.9068 |
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+ | 0.0001 | 21.0 | 4725 | 0.8115 | 0.8968 |
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+ | 0.0178 | 22.0 | 4950 | 0.8547 | 0.9118 |
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+ | 0.0024 | 23.0 | 5175 | 0.8065 | 0.9135 |
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+ | 0.0 | 24.0 | 5400 | 0.8301 | 0.9085 |
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+ | 0.0494 | 25.0 | 5625 | 0.9352 | 0.9035 |
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+ | 0.0 | 26.0 | 5850 | 0.9033 | 0.8902 |
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+ | 0.0001 | 27.0 | 6075 | 0.8768 | 0.8918 |
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+ | 0.0 | 28.0 | 6300 | 0.8873 | 0.9002 |
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+ | 0.0 | 29.0 | 6525 | 0.8635 | 0.9018 |
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+ | 0.007 | 30.0 | 6750 | 0.8770 | 0.8952 |
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+ | 0.0055 | 31.0 | 6975 | 0.9657 | 0.8985 |
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+ | 0.0 | 32.0 | 7200 | 0.9004 | 0.8935 |
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+ | 0.0016 | 33.0 | 7425 | 0.9326 | 0.8918 |
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+ | 0.003 | 34.0 | 7650 | 0.9751 | 0.9002 |
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+ | 0.0 | 35.0 | 7875 | 0.9491 | 0.9052 |
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+ | 0.0043 | 36.0 | 8100 | 0.9618 | 0.8952 |
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+ | 0.0087 | 37.0 | 8325 | 0.9634 | 0.8902 |
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+ | 0.0294 | 38.0 | 8550 | 1.0166 | 0.8952 |
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+ | 0.0044 | 39.0 | 8775 | 0.9519 | 0.8968 |
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+ | 0.0 | 40.0 | 9000 | 0.9467 | 0.8985 |
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+ | 0.0001 | 41.0 | 9225 | 0.9520 | 0.9002 |
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+ | 0.0002 | 42.0 | 9450 | 0.9492 | 0.9002 |
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+ | 0.0 | 43.0 | 9675 | 0.9742 | 0.8985 |
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+ | 0.0 | 44.0 | 9900 | 1.0125 | 0.9018 |
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+ | 0.0 | 45.0 | 10125 | 0.9921 | 0.9002 |
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+ | 0.0 | 46.0 | 10350 | 0.9848 | 0.9002 |
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+ | 0.0 | 47.0 | 10575 | 0.9786 | 0.8985 |
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+ | 0.0 | 48.0 | 10800 | 0.9903 | 0.9002 |
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+ | 0.0039 | 49.0 | 11025 | 0.9883 | 0.9002 |
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+ | 0.0003 | 50.0 | 11250 | 0.9909 | 0.9002 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.1
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.2
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