Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use Dax161/Salta1v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dax161/Salta1v with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dax161/Salta1v") 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("Dax161/Salta1v") model = AutoModelForImageClassification.from_pretrained("Dax161/Salta1v", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Dax161/Salta1v: direct link, hf CLI and curl.
- Browser
- Download file 464 Bytes
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https://huggingface.co/Dax161/Salta1v/resolve/main/README.md
- Command line
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hf download hf://Dax161/Salta1v/README.md
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curl -L -o README.md https://huggingface.co/Dax161/Salta1v/resolve/main/README.md
464 Bytes
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Salta1v | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 1.0 | |
| # Salta1v | |
| ## Example Images | |
| #### Causa rellena | |
|  | |
| #### Lomo saltado con arroz | |
|  |