Instructions to use ProbeX/Model-J__ResNet__model_idx_0051 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_0051 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_0051") 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_0051") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0051") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f70b8e7f2255a42ac4a07d4906231340c4d3e1092d461a60fb6cdae9c86e969
- Size of remote file:
- 5.37 kB
- SHA256:
- 85aad9312d8942e6df4b01dfc8496d6ae928098429fac62de715f88dbc14896c
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