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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: ViTForImageClassification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ViTForImageClassification |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1199 |
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- Accuracy: 0.9678 |
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## Model description |
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[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) |
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## Training and evaluation data |
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[CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) |
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## Training procedure |
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Straightforward tuning of all model's parameters. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 128 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.2995 | 0.27 | 100 | 0.3419 | 0.9108 | |
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| 0.2289 | 0.53 | 200 | 0.2482 | 0.9288 | |
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| 0.1811 | 0.8 | 300 | 0.2139 | 0.9357 | |
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| 0.0797 | 1.07 | 400 | 0.1813 | 0.946 | |
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| 0.1128 | 1.33 | 500 | 0.1741 | 0.9452 | |
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| 0.086 | 1.6 | 600 | 0.1659 | 0.9513 | |
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| 0.0815 | 1.87 | 700 | 0.1468 | 0.9547 | |
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| 0.048 | 2.13 | 800 | 0.1393 | 0.9592 | |
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| 0.021 | 2.4 | 900 | 0.1399 | 0.9603 | |
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| 0.0271 | 2.67 | 1000 | 0.1334 | 0.9642 | |
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| 0.0231 | 2.93 | 1100 | 0.1228 | 0.9658 | |
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| 0.0101 | 3.2 | 1200 | 0.1229 | 0.9673 | |
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| 0.0041 | 3.47 | 1300 | 0.1189 | 0.9675 | |
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| 0.0043 | 3.73 | 1400 | 0.1165 | 0.9683 | |
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| 0.0067 | 4.0 | 1500 | 0.1145 | 0.9697 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.14.1 |
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