Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use Harshmuriki/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harshmuriki/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Harshmuriki/test2") 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("Harshmuriki/test2") model = AutoModelForImageClassification.from_pretrained("Harshmuriki/test2") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Harshmuriki/test2")
model = AutoModelForImageClassification.from_pretrained("Harshmuriki/test2")Quick Links
test2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
banana
green bell pepper
red hot pepper
- Downloads last month
- 5
Evaluation results
- Accuracyself-reported0.806



# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Harshmuriki/test2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")