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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-batch-32 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: vuongnhathien/30VNFoods |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8755952380952381 |
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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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# vit-base-batch-32 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the vuongnhathien/30VNFoods dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6201 |
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- Accuracy: 0.8756 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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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: cosine |
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- num_epochs: 10 |
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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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| 0.6735 | 1.0 | 550 | 0.8003 | 0.7583 | |
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| 0.4048 | 2.0 | 1100 | 0.6471 | 0.8266 | |
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| 0.2506 | 3.0 | 1650 | 0.6220 | 0.8354 | |
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| 0.1521 | 4.0 | 2200 | 0.6406 | 0.8493 | |
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| 0.0812 | 5.0 | 2750 | 0.6855 | 0.8545 | |
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| 0.0279 | 6.0 | 3300 | 0.6767 | 0.8648 | |
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| 0.0094 | 7.0 | 3850 | 0.6252 | 0.8744 | |
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| 0.0074 | 8.0 | 4400 | 0.6064 | 0.8751 | |
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| 0.0056 | 9.0 | 4950 | 0.5997 | 0.8783 | |
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| 0.0016 | 10.0 | 5500 | 0.6009 | 0.8767 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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