Instructions to use ProbeX/Model-J__ResNet__model_idx_0701 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_0701 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_0701") 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_0701") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0701", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 892a12b1455191971eff7247eddf2ce24ad581ed3a46a05ab49897724de9a89f
- Size of remote file:
- 171 MB
- SHA256:
- a166dd937444773e702a912be32d68fabf3a351d4dd54428c6482b3675ea0b57
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