Instructions to use ProbeX/Model-J__ResNet__model_idx_0959 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_0959 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_0959") 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_0959") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0959") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4bdce7c30c8208220742b81291572cf0fa68c22f460faeebeef3659da27359c
- Size of remote file:
- 171 MB
- SHA256:
- b9cb35cf850f18552bf83004da76772a6bc7558350af5dc63ee95ec0bb106457
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