| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - image_folder | |
| metrics: | |
| - accuracy | |
| base_model: google/vit-base-patch16-224-in21k | |
| model-index: | |
| - name: vit-base-patch16-224-in21k-finetuned-cifar10 | |
| results: | |
| - task: | |
| type: image-classification | |
| name: Image Classification | |
| dataset: | |
| name: image_folder | |
| type: image_folder | |
| args: default | |
| metrics: | |
| - type: accuracy | |
| value: 0.9881481481481481 | |
| name: Accuracy | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # vit-base-patch16-224-in21k-finetuned-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 image_folder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1357 | |
| - Accuracy: 0.9881 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2455 | 1.0 | 190 | 0.2227 | 0.9830 | | |
| | 0.1363 | 2.0 | 380 | 0.1357 | 0.9881 | | |
| | 0.0954 | 3.0 | 570 | 0.1194 | 0.9878 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 2.0.0 | |
| - Tokenizers 0.11.6 | |