--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: finetuned-food results: - task: name: Image Classification type: image-classification dataset: name: food_images_classification type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9281675392670157 --- # finetuned-food 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 food_images_classification dataset. It achieves the following results on the evaluation set: - Loss: 0.2816 - Accuracy: 0.9282 ## 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: 0.0002 - train_batch_size: 15 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8456 | 0.39 | 500 | 0.8593 | 0.7634 | | 0.7824 | 0.78 | 1000 | 0.6625 | 0.8172 | | 0.4806 | 1.18 | 1500 | 0.4951 | 0.8618 | | 0.6206 | 1.57 | 2000 | 0.4434 | 0.88 | | 0.5096 | 1.96 | 2500 | 0.4937 | 0.8683 | | 0.4576 | 2.35 | 3000 | 0.4060 | 0.8907 | | 0.3284 | 2.75 | 3500 | 0.3414 | 0.9081 | | 0.2022 | 3.14 | 4000 | 0.3330 | 0.9118 | | 0.1332 | 3.53 | 4500 | 0.3043 | 0.9208 | | 0.1821 | 3.92 | 5000 | 0.2816 | 0.9282 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0.post100 - Datasets 2.12.0 - Tokenizers 0.13.2