Instructions to use ProbeX/Model-J__ResNet__model_idx_0022 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_0022 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_0022") 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_0022") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0022") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 265a5bb37c9efd2891b935048239e8f56cabc76cc1e845595bc029984ece9403
- Size of remote file:
- 5.37 kB
- SHA256:
- 4247ec1391d1d640896b9b63ba992c1736fa9956c73926aff127564d45ddf1ac
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