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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-152 |
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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: Resnet152-30VN |
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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: 0.8353174603174603 |
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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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# Resnet152-30VN |
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This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the vuongnhathien/30VNFoods dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5769 |
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- Accuracy: 0.8353 |
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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: 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: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 1.4198 | 1.0 | 275 | 0.7348 | 0.8741 | |
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| 0.565 | 2.0 | 550 | 0.8119 | 0.6347 | |
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| 0.2846 | 3.0 | 825 | 0.8310 | 0.6003 | |
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| 0.1727 | 4.0 | 1100 | 0.8410 | 0.6041 | |
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| 0.0835 | 5.0 | 1375 | 0.8461 | 0.6464 | |
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| 0.0534 | 6.0 | 1650 | 0.8565 | 0.6776 | |
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| 0.0283 | 7.0 | 1925 | 0.7107 | 0.8501 | |
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| 0.0186 | 8.0 | 2200 | 0.7066 | 0.8620 | |
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| 0.0111 | 9.0 | 2475 | 0.6772 | 0.8648 | |
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| 0.0096 | 10.0 | 2750 | 0.6898 | 0.8628 | |
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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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