Instructions to use ProbeX/Model-J__ResNet__model_idx_0968 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_0968 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_0968") 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_0968") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0968") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd1b780e00b07927dc32ede4492d0a8a0a1735160bb531e5b41b8a43db9d9d13
- Size of remote file:
- 5.37 kB
- SHA256:
- 9f890672d4a455035a4bd5decb984aef157f35d691441981ba42cf719b546feb
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