Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use holyspark/Moto_Classification_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use holyspark/Moto_Classification_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="holyspark/Moto_Classification_v2") 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("holyspark/Moto_Classification_v2") model = AutoModelForImageClassification.from_pretrained("holyspark/Moto_Classification_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/Benz_Motos.jpg from holyspark/Moto_Classification_v2: direct link, hf CLI and curl.
- Browser
- Download file 44.7 kB
-
https://huggingface.co/holyspark/Moto_Classification_v2/resolve/main/images/Benz_Motos.jpg
- Command line
-
hf download hf://holyspark/Moto_Classification_v2/images/Benz_Motos.jpg
-
curl -L -o Benz_Motos.jpg https://huggingface.co/holyspark/Moto_Classification_v2/resolve/main/images/Benz_Motos.jpg
44.7 kB

- Xet hash:
- ed48192de962cf313d3ccba0b4bb74af8edf9cc0d37632f72f62e144eba13bff
- Size of remote file:
- 44.7 kB
- SHA256:
- 10847ffbfe6637b66a2d376a201c47599cfd9b1350b96df996cb5e2faec2cda7
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