Instructions to use ProbeX/Model-J__ResNet__model_idx_0452 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_0452 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_0452") 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_0452") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0452", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64e453837fd6e2a733a2db19729dbadc071e71aae8243a6b0568290155104e40
- Size of remote file:
- 5.37 kB
- SHA256:
- d00d0d21c9091040267b0b2d15df124e12844a4788589bc0222fae74c59914b7
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