Instructions to use smc/PANDA_ConvNeXT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smc/PANDA_ConvNeXT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="smc/PANDA_ConvNeXT") 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("smc/PANDA_ConvNeXT") model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ConvNeXT") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("smc/PANDA_ConvNeXT")
model = AutoModelForImageClassification.from_pretrained("smc/PANDA_ConvNeXT")Quick Links
- Downloads last month
- 12
Evaluation results
- Accuracyself-reported0.549
- Quadratic Cohen's Kappaself-reported0.663

ISUP 2:
ISUP 3:
ISUP 4:
ISUP 5:

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