Instructions to use ProbeX/Model-J__ResNet__model_idx_0185 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_0185 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_0185") 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_0185") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0185", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b738c3aa3fff45117b4940342a08f5ef6164ebe98f294b29252ae504f5f6c53
- Size of remote file:
- 171 MB
- SHA256:
- d62b610059ca8bbff1780424cc6b0d619bfc37983074817d9123f2f8a3307b28
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