Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use taufeeq28/vehicles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taufeeq28/vehicles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="taufeeq28/vehicles") 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("taufeeq28/vehicles") model = AutoModelForImageClassification.from_pretrained("taufeeq28/vehicles", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/cycles.jpg from taufeeq28/vehicles: direct link, hf CLI and curl.
- Browser
- Download file 34.2 kB
-
https://huggingface.co/taufeeq28/vehicles/resolve/main/images/cycles.jpg
- Command line
-
hf download hf://taufeeq28/vehicles/images/cycles.jpg
-
curl -L -o cycles.jpg https://huggingface.co/taufeeq28/vehicles/resolve/main/images/cycles.jpg
34.2 kB

- Xet hash:
- b9c6068881f1dc7624e3e676720dbe666c93c2ef2dd14c2a0685acd3199c4110
- Size of remote file:
- 34.2 kB
- SHA256:
- eb28424371b6920ec8887d456f4670fc4571353e5adb884f65b333965e9f178b
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