Instructions to use ProbeX/Model-J__ResNet__model_idx_0910 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_0910 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_0910") 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_0910") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0910", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 704b48460a10d64153208fee875f176f24d963be7abc96dae277baa54f760fa9
- Size of remote file:
- 5.37 kB
- SHA256:
- 4fbb9e9de481924339e8aa419248c195d9952710d724dbd66b997e007132485d
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