Instructions to use ProbeX/Model-J__ResNet__model_idx_0697 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_0697 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_0697") 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_0697") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0697", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba29364b5b4b374bf160aec9af029c21a1943e3822e5a773191e9b81ce0d5f7c
- Size of remote file:
- 5.37 kB
- SHA256:
- 8dfe9fb812ce93eb23693b67d9d051ad69a81a60028444b93fda4c68a03ac7ad
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