Instructions to use ProbeX/Model-J__ResNet__model_idx_0950 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_0950 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_0950") 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_0950") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0950", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cecfc3724f6526696e92baa707e554a578a1c6e17eb145d78c81d4ec029ddae
- Size of remote file:
- 171 MB
- SHA256:
- 5afe0ce827a0240f8a2e614ccb13842a06b09a8ae36ef3fee4bc8dc2f49093fd
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