Instructions to use ProbeX/Model-J__ResNet__model_idx_0228 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_0228 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_0228") 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_0228") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0228") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6290849c3e5397a09af1a5fafdce9d6996ff215ea75336049b70982371c1c97
- Size of remote file:
- 5.37 kB
- SHA256:
- ff5141e6a9fbdeff21ef6f0f1ebc70721ecf5361cb053e49d9a5bb7313b37b0f
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