Instructions to use ProbeX/Model-J__ResNet__model_idx_0467 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_0467 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_0467") 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_0467") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0467", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ed5e67d255cb09e1288463f25e397dc895fb7a75abedbab90a952c8f8d1ba5b
- Size of remote file:
- 171 MB
- SHA256:
- 84505ff177a881dfeef89993dfdfd36a4bfa87add63935451140f8363559d1ad
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