Instructions to use ProbeX/Model-J__ResNet__model_idx_0843 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_0843 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_0843") 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_0843") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0843", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9cee0fa4c8bb08b50026204e620e67c411f1f4b29d34c4b3ebd34cd77c23891
- Size of remote file:
- 5.37 kB
- SHA256:
- 98190789827c0822c70001fe1375528ecb9ae8e7308522faeef2cf9a42ee674e
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