Instructions to use ProbeX/Model-J__ResNet__model_idx_0541 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_0541 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_0541") 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_0541") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0541", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fa8960067c1718daa3898a71d9c7cf4e7baafc096ebc15720c5547ef9661363
- Size of remote file:
- 171 MB
- SHA256:
- 0977de0f620a1c0f05e7a331095a45d8059c56a06268ca288260e38801cd4675
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