Instructions to use ProbeX/Model-J__ResNet__model_idx_0342 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_0342 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_0342") 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_0342") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0342", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82a53ef7dde2fbf7dc9bad4c152361c6cb0d363c817a5f98af2a4fe8c4eab9fe
- Size of remote file:
- 171 MB
- SHA256:
- 728ebd6a5c492383ce2be67c5b60244c48e69ce71129fe10dba7542e99f9e51e
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