Instructions to use ProbeX/Model-J__ResNet__model_idx_0573 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_0573 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_0573") 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_0573") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0573", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75e1193f57ff63c064ad123dd18dede7f83640695eb0b393d5159b66927777c9
- Size of remote file:
- 171 MB
- SHA256:
- 1c4be2ec07c41f5b43956a41d195b6088bdc664af40b68194d65edfa7784ebc7
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