| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: ViTForImageClassification |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # ViTForImageClassification |
| |
|
| | 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](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1199 |
| | - Accuracy: 0.9678 |
| |
|
| | ## Model description |
| |
|
| | [A detailed description of model architecture can be found here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/modeling_vit.py#L756) |
| |
|
| | ## Training and evaluation data |
| |
|
| | [CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) |
| |
|
| | ## Training procedure |
| | Straightforward tuning of all model's parameters. |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0002 |
| | - train_batch_size: 128 |
| | - eval_batch_size: 64 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 0.2995 | 0.27 | 100 | 0.3419 | 0.9108 | |
| | | 0.2289 | 0.53 | 200 | 0.2482 | 0.9288 | |
| | | 0.1811 | 0.8 | 300 | 0.2139 | 0.9357 | |
| | | 0.0797 | 1.07 | 400 | 0.1813 | 0.946 | |
| | | 0.1128 | 1.33 | 500 | 0.1741 | 0.9452 | |
| | | 0.086 | 1.6 | 600 | 0.1659 | 0.9513 | |
| | | 0.0815 | 1.87 | 700 | 0.1468 | 0.9547 | |
| | | 0.048 | 2.13 | 800 | 0.1393 | 0.9592 | |
| | | 0.021 | 2.4 | 900 | 0.1399 | 0.9603 | |
| | | 0.0271 | 2.67 | 1000 | 0.1334 | 0.9642 | |
| | | 0.0231 | 2.93 | 1100 | 0.1228 | 0.9658 | |
| | | 0.0101 | 3.2 | 1200 | 0.1229 | 0.9673 | |
| | | 0.0041 | 3.47 | 1300 | 0.1189 | 0.9675 | |
| | | 0.0043 | 3.73 | 1400 | 0.1165 | 0.9683 | |
| | | 0.0067 | 4.0 | 1500 | 0.1145 | 0.9697 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.34.1 |
| | - Pytorch 2.0.1+cu117 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.14.1 |
| |
|
