Instructions to use ProbeX/Model-J__ResNet__model_idx_0399 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_0399 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_0399") 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_0399") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0399", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcf15656960d65d3ae2f424667dd9bf0063e9707918f0d50c438bf9f961472b8
- Size of remote file:
- 5.37 kB
- SHA256:
- 29a865d601514c88ad4051e59d0446908d45d3184b4af106c5561da08d252c16
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