--- license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: vit_finedtuned_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:5000] args: default metrics: - name: Accuracy type: accuracy value: 0.934 --- # vit_finedtuned_food_model This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.2214 - Accuracy: 0.934 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6064 | 0.99 | 62 | 0.4593 | 0.872 | | 0.2607 | 2.0 | 125 | 0.2649 | 0.92 | | 0.2074 | 2.99 | 187 | 0.2421 | 0.924 | | 0.0908 | 4.0 | 250 | 0.2442 | 0.921 | | 0.1237 | 4.99 | 312 | 0.2353 | 0.923 | | 0.0915 | 6.0 | 375 | 0.2402 | 0.923 | | 0.0549 | 6.99 | 437 | 0.2053 | 0.933 | | 0.0645 | 8.0 | 500 | 0.2190 | 0.929 | | 0.087 | 8.99 | 562 | 0.2313 | 0.935 | | 0.0544 | 9.92 | 620 | 0.2214 | 0.934 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3