Instructions to use ProbeX/Model-J__ResNet__model_idx_0071 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_0071 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_0071") 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_0071") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0071", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d919c8018924c5988b0d554b2e8c529705435bc15eb7164a416c643a22a97a1
- Size of remote file:
- 5.37 kB
- SHA256:
- 45615ff2880bca6b26be0053869392d4b51813dae1b6589c68d6a770e261986f
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