Instructions to use Moreza009/HF_CVcourse_FoodClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moreza009/HF_CVcourse_FoodClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Moreza009/HF_CVcourse_FoodClassifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier") model = AutoModelForImageClassification.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Moreza009/HF_CVcourse_FoodClassifier: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
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https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/README.md
- Command line
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hf download hf://Moreza009/HF_CVcourse_FoodClassifier/README.md
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curl -L -o README.md https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/README.md
1.71 kB
| license: apache-2.0 | |
| base_model: microsoft/swin-tiny-patch4-window7-224 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: swin-tiny-patch4-window7-224-finetuned-eurosat | |
| results: [] | |
| language: | |
| - en | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # swin-tiny-patch4-window7-224-finetuned-eurosat | |
| This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on [ethz/food101](https://huggingface.co/datasets/ethz/food101) dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9383 | |
| - Accuracy: 0.7506 | |
| ## 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: 5e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 1.3766 | 0.9986 | 532 | 1.1204 | 0.7072 | | |
| | 1.2542 | 1.9972 | 1064 | 0.9383 | 0.7506 | | |
| ### Framework versions | |
| - Transformers 4.41.1 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 |