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smids_3x_beit_base_rms_0001_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_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: 0.9001663893510815
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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_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: 1.0800
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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: 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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+ | 0.4112 | 1.0 | 225 | 0.4131 | 0.8602 |
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+ | 0.2912 | 2.0 | 450 | 0.4363 | 0.8369 |
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+ | 0.1579 | 3.0 | 675 | 0.3616 | 0.8752 |
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+ | 0.187 | 4.0 | 900 | 0.2854 | 0.8852 |
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+ | 0.1222 | 5.0 | 1125 | 0.4884 | 0.8835 |
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+ | 0.0818 | 6.0 | 1350 | 0.4361 | 0.8885 |
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+ | 0.0981 | 7.0 | 1575 | 0.4218 | 0.8769 |
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+ | 0.1165 | 8.0 | 1800 | 0.5449 | 0.8702 |
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+ | 0.0593 | 9.0 | 2025 | 0.5250 | 0.9002 |
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+ | 0.0725 | 10.0 | 2250 | 0.5111 | 0.8985 |
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+ | 0.0275 | 11.0 | 2475 | 0.5486 | 0.8702 |
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+ | 0.0408 | 12.0 | 2700 | 0.6442 | 0.8852 |
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+ | 0.0654 | 13.0 | 2925 | 0.6277 | 0.8968 |
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+ | 0.0139 | 14.0 | 3150 | 0.6248 | 0.8918 |
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+ | 0.0393 | 15.0 | 3375 | 0.5753 | 0.8935 |
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+ | 0.0368 | 16.0 | 3600 | 0.6499 | 0.8902 |
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+ | 0.0316 | 17.0 | 3825 | 0.6023 | 0.8918 |
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+ | 0.0193 | 18.0 | 4050 | 0.7084 | 0.8952 |
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+ | 0.001 | 19.0 | 4275 | 0.7253 | 0.9002 |
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+ | 0.0578 | 20.0 | 4500 | 0.7248 | 0.8785 |
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+ | 0.08 | 21.0 | 4725 | 0.6832 | 0.8902 |
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+ | 0.0213 | 22.0 | 4950 | 0.8468 | 0.8902 |
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+ | 0.008 | 23.0 | 5175 | 0.8669 | 0.8935 |
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+ | 0.0041 | 24.0 | 5400 | 0.8402 | 0.8802 |
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+ | 0.0205 | 25.0 | 5625 | 0.8106 | 0.8869 |
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+ | 0.0196 | 26.0 | 5850 | 0.8576 | 0.8902 |
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+ | 0.0001 | 27.0 | 6075 | 0.7352 | 0.8985 |
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+ | 0.0003 | 28.0 | 6300 | 0.7339 | 0.9018 |
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+ | 0.0078 | 29.0 | 6525 | 0.8497 | 0.8985 |
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+ | 0.007 | 30.0 | 6750 | 1.0378 | 0.8802 |
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+ | 0.0063 | 31.0 | 6975 | 0.9737 | 0.8902 |
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+ | 0.0147 | 32.0 | 7200 | 0.9357 | 0.8902 |
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+ | 0.0358 | 33.0 | 7425 | 0.9702 | 0.8885 |
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+ | 0.0003 | 34.0 | 7650 | 0.7989 | 0.8902 |
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+ | 0.0001 | 35.0 | 7875 | 0.9353 | 0.8885 |
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+ | 0.0032 | 36.0 | 8100 | 0.8664 | 0.8952 |
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+ | 0.0015 | 37.0 | 8325 | 0.7955 | 0.8968 |
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+ | 0.0 | 38.0 | 8550 | 0.8664 | 0.8952 |
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+ | 0.0222 | 39.0 | 8775 | 0.9521 | 0.8985 |
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+ | 0.0042 | 40.0 | 9000 | 0.9427 | 0.8985 |
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+ | 0.0027 | 41.0 | 9225 | 0.9502 | 0.9002 |
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+ | 0.0 | 42.0 | 9450 | 1.0516 | 0.8935 |
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+ | 0.0 | 43.0 | 9675 | 0.9695 | 0.8952 |
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+ | 0.0 | 44.0 | 9900 | 1.0122 | 0.8985 |
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+ | 0.0 | 45.0 | 10125 | 0.9974 | 0.8985 |
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+ | 0.0 | 46.0 | 10350 | 1.0109 | 0.9002 |
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+ | 0.0 | 47.0 | 10575 | 1.0770 | 0.8952 |
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+ | 0.0 | 48.0 | 10800 | 1.0946 | 0.8985 |
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+ | 0.0025 | 49.0 | 11025 | 1.0859 | 0.9002 |
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+ | 0.002 | 50.0 | 11250 | 1.0800 | 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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