Instructions to use ProbeX/Model-J__ResNet__model_idx_0134 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_0134 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_0134") 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_0134") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0134") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8c731148b53fe237e4c6d902a4b9fac6a78da6504e885c3c241028646fd1194
- Size of remote file:
- 5.37 kB
- SHA256:
- f5a06f60f7ee55ba67b263a0dff9777331e4a07e3039bacbb9d12987a33069cf
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