Instructions to use ProbeX/Model-J__ResNet__model_idx_0684 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_0684 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_0684") 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_0684") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0684") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdb88b65b920707aa903f0b9942b6731ff76c8672a12d427c10bc4d00becb862
- Size of remote file:
- 5.37 kB
- SHA256:
- 78d3852060e3b1de890065814496393b49288612aea20d06ea057ef1ab5c50ce
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