--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - cifar10 model-index: - name: vit-base-patch16-224-cifar10 results: - task: type: image-classification name: Image Classification dataset: name: cifar10 type: cifar10 config: plain_text split: test metrics: - name: Accuracy type: accuracy value: 0.1004 verified: true - name: Precision Macro type: precision value: 0.07725693204097324 verified: true - name: Precision Micro type: precision value: 0.1004 verified: true - name: Precision Weighted type: precision value: 0.07725693204097323 verified: true - name: Recall Macro type: recall value: 0.1004 verified: true - name: Recall Micro type: recall value: 0.1004 verified: true - name: Recall Weighted type: recall value: 0.1004 verified: true - name: F1 Macro type: f1 value: 0.07942008420616108 verified: true - name: F1 Micro type: f1 value: 0.1004 verified: true - name: F1 Weighted type: f1 value: 0.07942008420616108 verified: true - name: loss type: loss value: 2.3154706954956055 verified: true --- # vit-base-patch16-224-cifar10 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 cifar10 dataset. ## 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: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.10.1 - Datasets 2.1.0 - Tokenizers 0.12.1