Instructions to use ProbeX/Model-J__ResNet__model_idx_0202 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_0202 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_0202") 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_0202") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0202", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1dbe61ac221814f33843ee84fda9ce773fdafce194db39cd6f5c64a889623673
- Size of remote file:
- 5.37 kB
- SHA256:
- fa853e24bec1e28a6e7bc77e838b98a34265eb3594f495ee1dc2ceabae24a208
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