Instructions to use ProbeX/Model-J__ResNet__model_idx_0549 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_0549 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_0549") 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_0549") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0549", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c495ddd3cd66f15a9300b101dc2b3b5c7a722fe5e8e8acf23e12e74823fae01a
- Size of remote file:
- 171 MB
- SHA256:
- 5ad098427d14c31880e8d89bd56bdead3cb1eaa1c1c5204e1af32602659005e6
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