Instructions to use ProbeX/Model-J__ResNet__model_idx_0030 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_0030 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_0030") 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_0030") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0030", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab4d37c9f2df243bc5fee661491647a85c07cccb568c9ca1325cdc9eb8987684
- Size of remote file:
- 5.37 kB
- SHA256:
- ace438cb6cabeff5a1b80b291a025ffc7d1f19379d7492196f2101177b6d6249
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