--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.5875 --- # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2378 - Accuracy: 0.5875 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 2.0656 | 0.125 | | No log | 2.0 | 80 | 2.0558 | 0.1938 | | No log | 3.0 | 120 | 2.0177 | 0.2375 | | No log | 4.0 | 160 | 1.9156 | 0.3438 | | No log | 5.0 | 200 | 1.7849 | 0.3063 | | No log | 6.0 | 240 | 1.6961 | 0.3187 | | No log | 7.0 | 280 | 1.6026 | 0.3937 | | No log | 8.0 | 320 | 1.5455 | 0.3688 | | No log | 9.0 | 360 | 1.4723 | 0.4562 | | No log | 10.0 | 400 | 1.3931 | 0.5 | | No log | 11.0 | 440 | 1.4418 | 0.4375 | | No log | 12.0 | 480 | 1.3306 | 0.4437 | | 1.5855 | 13.0 | 520 | 1.2437 | 0.575 | | 1.5855 | 14.0 | 560 | 1.3712 | 0.4875 | | 1.5855 | 15.0 | 600 | 1.2102 | 0.55 | | 1.5855 | 16.0 | 640 | 1.3217 | 0.5188 | | 1.5855 | 17.0 | 680 | 1.3656 | 0.4938 | | 1.5855 | 18.0 | 720 | 1.3261 | 0.525 | | 1.5855 | 19.0 | 760 | 1.5611 | 0.4625 | | 1.5855 | 20.0 | 800 | 1.4503 | 0.5125 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3