Instructions to use ProbeX/Model-J__ResNet__model_idx_0069 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_0069 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_0069") 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_0069") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0069", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffb5aeb5bf7b85dbda68ba899bbc62c86f6998b922fb85968adacd21dc8b8885
- Size of remote file:
- 171 MB
- SHA256:
- 6add192e5644d33ac3ef8c46c4d6ba6ee5a8a7329069e1a5f1935be5359ac043
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