Instructions to use ProbeX/Model-J__ResNet__model_idx_0215 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_0215 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_0215") 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_0215") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0215", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08f3ec3f4158ed6c070a2ec89c311e3f0fd758a9573b0624b5836f53d919e737
- Size of remote file:
- 171 MB
- SHA256:
- d5951295f3cfe0b41e2d70797f7ac8a38f9594e13b0a7e9e0b5fc93d6f2946bb
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