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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-seed-1e-4 |
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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: 0.8898809523809523 |
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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-seed-1e-4 |
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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: 0.3908 |
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- Accuracy: 0.8899 |
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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: 0.0001 |
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- train_batch_size: 64 |
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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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| 0.5402 | 1.0 | 275 | 0.4615 | 0.8644 | |
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| 0.2057 | 2.0 | 550 | 0.4198 | 0.8839 | |
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| 0.0669 | 3.0 | 825 | 0.4860 | 0.8744 | |
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| 0.0281 | 4.0 | 1100 | 0.4557 | 0.8879 | |
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| 0.0076 | 5.0 | 1375 | 0.4301 | 0.8998 | |
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| 0.0079 | 6.0 | 1650 | 0.4535 | 0.9002 | |
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| 0.0042 | 7.0 | 1925 | 0.4320 | 0.9058 | |
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| 0.0037 | 8.0 | 2200 | 0.4294 | 0.9062 | |
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| 0.0017 | 9.0 | 2475 | 0.4316 | 0.9066 | |
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| 0.0029 | 10.0 | 2750 | 0.4318 | 0.9070 | |
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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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