Instructions to use ProbeX/Model-J__ResNet__model_idx_0475 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_0475 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_0475") 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_0475") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0475", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81bfbfef7021a1e6b8ed69c0c47cf69ad7ce07b6e3aebd7cb110b51dc2c0617a
- Size of remote file:
- 5.37 kB
- SHA256:
- 14bdd13ec0fe44f49b69edf84745603ee73145897682699ef4ce93f24f5dd677
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