Instructions to use ProbeX/Model-J__ResNet__model_idx_0987 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_0987 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_0987") 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_0987") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0987", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e85d1753efaf953ed192600d495c3c45b42fc41127c521b3d1e945c84e22d06a
- Size of remote file:
- 171 MB
- SHA256:
- 10a0e792618f325ed8c1993a2f8a3faea68db5278ed6587f990e344d49fa2610
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