Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use vuongnhathien/Resnet152-30VN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuongnhathien/Resnet152-30VN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vuongnhathien/Resnet152-30VN") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("vuongnhathien/Resnet152-30VN") model = AutoModelForImageClassification.from_pretrained("vuongnhathien/Resnet152-30VN") - Notebooks
- Google Colab
- Kaggle
Resnet152-30VN
This model is a fine-tuned version of microsoft/resnet-152 on the vuongnhathien/30VNFoods dataset. It achieves the following results on the evaluation set:
- Loss: 0.5769
- Accuracy: 0.8353
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.4198 | 1.0 | 275 | 0.7348 | 0.8741 |
| 0.565 | 2.0 | 550 | 0.8119 | 0.6347 |
| 0.2846 | 3.0 | 825 | 0.8310 | 0.6003 |
| 0.1727 | 4.0 | 1100 | 0.8410 | 0.6041 |
| 0.0835 | 5.0 | 1375 | 0.8461 | 0.6464 |
| 0.0534 | 6.0 | 1650 | 0.8565 | 0.6776 |
| 0.0283 | 7.0 | 1925 | 0.7107 | 0.8501 |
| 0.0186 | 8.0 | 2200 | 0.7066 | 0.8620 |
| 0.0111 | 9.0 | 2475 | 0.6772 | 0.8648 |
| 0.0096 | 10.0 | 2750 | 0.6898 | 0.8628 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for vuongnhathien/Resnet152-30VN
Base model
microsoft/resnet-152Evaluation results
- Accuracy on vuongnhathien/30VNFoodsvalidation set self-reported0.835