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hushem_5x_beit_base_sgd_001_fold5

Image Classification
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  2. model.safetensors +1 -1
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: hushem_5x_beit_base_sgd_001_fold5
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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: 0.7073170731707317
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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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+ # hushem_5x_beit_base_sgd_001_fold5
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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: 0.8979
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+ - Accuracy: 0.7073
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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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+ | 1.5363 | 1.0 | 28 | 1.5515 | 0.2439 |
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+ | 1.4164 | 2.0 | 56 | 1.4971 | 0.3171 |
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+ | 1.362 | 3.0 | 84 | 1.4523 | 0.3659 |
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+ | 1.3381 | 4.0 | 112 | 1.3987 | 0.3902 |
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+ | 1.2805 | 5.0 | 140 | 1.3538 | 0.4390 |
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+ | 1.286 | 6.0 | 168 | 1.3184 | 0.4634 |
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+ | 1.2664 | 7.0 | 196 | 1.2890 | 0.4634 |
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+ | 1.1752 | 8.0 | 224 | 1.2605 | 0.4878 |
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+ | 1.1615 | 9.0 | 252 | 1.2357 | 0.5366 |
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+ | 1.1583 | 10.0 | 280 | 1.2200 | 0.5366 |
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+ | 1.0622 | 11.0 | 308 | 1.1865 | 0.5854 |
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+ | 1.0888 | 12.0 | 336 | 1.1579 | 0.5854 |
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+ | 1.0774 | 13.0 | 364 | 1.1376 | 0.6098 |
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+ | 1.0639 | 14.0 | 392 | 1.1207 | 0.6098 |
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+ | 1.0329 | 15.0 | 420 | 1.1063 | 0.6098 |
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+ | 1.0224 | 16.0 | 448 | 1.0819 | 0.6098 |
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+ | 0.9888 | 17.0 | 476 | 1.0775 | 0.6098 |
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+ | 0.9619 | 18.0 | 504 | 1.0585 | 0.6098 |
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+ | 0.9636 | 19.0 | 532 | 1.0418 | 0.6098 |
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+ | 0.932 | 20.0 | 560 | 1.0228 | 0.6098 |
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+ | 0.9605 | 21.0 | 588 | 1.0275 | 0.6341 |
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+ | 0.9245 | 22.0 | 616 | 1.0007 | 0.6829 |
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+ | 0.9128 | 23.0 | 644 | 0.9952 | 0.6829 |
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+ | 0.8833 | 24.0 | 672 | 0.9964 | 0.6585 |
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+ | 0.8669 | 25.0 | 700 | 0.9755 | 0.6829 |
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+ | 0.8822 | 26.0 | 728 | 0.9683 | 0.6829 |
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+ | 0.8736 | 27.0 | 756 | 0.9644 | 0.6829 |
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+ | 0.8195 | 28.0 | 784 | 0.9503 | 0.6829 |
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+ | 0.815 | 29.0 | 812 | 0.9479 | 0.6829 |
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+ | 0.8347 | 30.0 | 840 | 0.9483 | 0.6829 |
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+ | 0.8334 | 31.0 | 868 | 0.9382 | 0.6829 |
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+ | 0.7989 | 32.0 | 896 | 0.9378 | 0.6829 |
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+ | 0.7878 | 33.0 | 924 | 0.9468 | 0.6829 |
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+ | 0.8479 | 34.0 | 952 | 0.9186 | 0.7073 |
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+ | 0.7868 | 35.0 | 980 | 0.9253 | 0.6829 |
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+ | 0.7921 | 36.0 | 1008 | 0.9134 | 0.7073 |
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+ | 0.7563 | 37.0 | 1036 | 0.9054 | 0.7073 |
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+ | 0.7507 | 38.0 | 1064 | 0.9071 | 0.7073 |
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+ | 0.8331 | 39.0 | 1092 | 0.8998 | 0.7073 |
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+ | 0.7104 | 40.0 | 1120 | 0.9052 | 0.7073 |
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+ | 0.773 | 41.0 | 1148 | 0.9044 | 0.7073 |
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+ | 0.719 | 42.0 | 1176 | 0.9021 | 0.7073 |
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+ | 0.7745 | 43.0 | 1204 | 0.8997 | 0.7073 |
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+ | 0.781 | 44.0 | 1232 | 0.9016 | 0.7073 |
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+ | 0.755 | 45.0 | 1260 | 0.9002 | 0.7073 |
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+ | 0.7437 | 46.0 | 1288 | 0.9000 | 0.7073 |
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+ | 0.7695 | 47.0 | 1316 | 0.8981 | 0.7073 |
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+ | 0.7339 | 48.0 | 1344 | 0.8980 | 0.7073 |
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+ | 0.7563 | 49.0 | 1372 | 0.8979 | 0.7073 |
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+ | 0.755 | 50.0 | 1400 | 0.8979 | 0.7073 |
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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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