--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_beit_base_adamax_0001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9066666666666666 --- # smids_5x_beit_base_adamax_0001_fold5 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. It achieves the following results on the evaluation set: - Loss: 1.0130 - Accuracy: 0.9067 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2609 | 1.0 | 375 | 0.4392 | 0.83 | | 0.2435 | 2.0 | 750 | 0.3135 | 0.87 | | 0.1555 | 3.0 | 1125 | 0.3719 | 0.885 | | 0.1553 | 4.0 | 1500 | 0.4465 | 0.89 | | 0.1 | 5.0 | 1875 | 0.4697 | 0.88 | | 0.0953 | 6.0 | 2250 | 0.5131 | 0.895 | | 0.1058 | 7.0 | 2625 | 0.5042 | 0.9033 | | 0.0095 | 8.0 | 3000 | 0.6171 | 0.9033 | | 0.0607 | 9.0 | 3375 | 0.5787 | 0.89 | | 0.0225 | 10.0 | 3750 | 0.6004 | 0.8983 | | 0.0522 | 11.0 | 4125 | 0.6713 | 0.89 | | 0.0354 | 12.0 | 4500 | 0.8122 | 0.9 | | 0.0281 | 13.0 | 4875 | 0.7547 | 0.8833 | | 0.0538 | 14.0 | 5250 | 0.6866 | 0.88 | | 0.0498 | 15.0 | 5625 | 0.7040 | 0.8783 | | 0.0034 | 16.0 | 6000 | 0.6946 | 0.8883 | | 0.0375 | 17.0 | 6375 | 0.7067 | 0.88 | | 0.0372 | 18.0 | 6750 | 0.8461 | 0.875 | | 0.0081 | 19.0 | 7125 | 0.5733 | 0.9 | | 0.025 | 20.0 | 7500 | 0.6029 | 0.9167 | | 0.0105 | 21.0 | 7875 | 0.6183 | 0.885 | | 0.0342 | 22.0 | 8250 | 0.5174 | 0.9217 | | 0.0291 | 23.0 | 8625 | 0.5708 | 0.9083 | | 0.0316 | 24.0 | 9000 | 0.7866 | 0.8833 | | 0.0002 | 25.0 | 9375 | 0.8031 | 0.895 | | 0.0548 | 26.0 | 9750 | 0.7954 | 0.8933 | | 0.0233 | 27.0 | 10125 | 0.8188 | 0.895 | | 0.0003 | 28.0 | 10500 | 0.7997 | 0.9 | | 0.0063 | 29.0 | 10875 | 0.8708 | 0.89 | | 0.0025 | 30.0 | 11250 | 0.8386 | 0.8967 | | 0.0008 | 31.0 | 11625 | 0.8998 | 0.8833 | | 0.0 | 32.0 | 12000 | 0.9085 | 0.8967 | | 0.0005 | 33.0 | 12375 | 0.7875 | 0.905 | | 0.0 | 34.0 | 12750 | 0.9329 | 0.8983 | | 0.0001 | 35.0 | 13125 | 0.7985 | 0.9017 | | 0.0 | 36.0 | 13500 | 0.8234 | 0.8983 | | 0.0 | 37.0 | 13875 | 0.8947 | 0.9033 | | 0.005 | 38.0 | 14250 | 0.9096 | 0.9067 | | 0.0291 | 39.0 | 14625 | 0.9293 | 0.9117 | | 0.0006 | 40.0 | 15000 | 0.8881 | 0.9117 | | 0.0 | 41.0 | 15375 | 1.0854 | 0.8967 | | 0.0003 | 42.0 | 15750 | 0.9486 | 0.8983 | | 0.0 | 43.0 | 16125 | 0.9324 | 0.91 | | 0.0 | 44.0 | 16500 | 0.9408 | 0.9083 | | 0.0 | 45.0 | 16875 | 1.0069 | 0.9067 | | 0.0 | 46.0 | 17250 | 1.0803 | 0.9 | | 0.013 | 47.0 | 17625 | 1.0261 | 0.905 | | 0.0 | 48.0 | 18000 | 1.0163 | 0.9067 | | 0.0 | 49.0 | 18375 | 1.0208 | 0.9083 | | 0.0021 | 50.0 | 18750 | 1.0130 | 0.9067 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2