Instructions to use ProbeX/Model-J__ResNet__model_idx_0572 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0572 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0572") 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("ProbeX/Model-J__ResNet__model_idx_0572") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0572") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92605eed16fbfd8554460517c8034c1ff27ae2f70540c0cf06d742cbf88cc638
- Size of remote file:
- 5.37 kB
- SHA256:
- a2a08e200f0fb11c6f91e1e87e3ec9967c6d83441de63fad2ced99d567de6251
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