Instructions to use ProbeX/Model-J__ResNet__model_idx_0992 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_0992 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_0992") 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_0992") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0992", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 149774bd458d90a3162eb2cc8006a386028446a128149fef4d49c9c74e9e73b0
- Size of remote file:
- 171 MB
- SHA256:
- 9f7aaa0b1d57e7475e9fbceec0f63729cdfb4d475c5011f239a7e07758f2a43d
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