Instructions to use ProbeX/Model-J__ResNet__model_idx_0965 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_0965 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_0965") 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_0965") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0965", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a16b9c3db3a0d6dff69379cf498cf32bfaac5c3cc4e25331faf296cdb80e1ca8
- Size of remote file:
- 171 MB
- SHA256:
- d1a918031a5819fb9b9352485e0d16b80d1d21f7a9d71b1404b6eb25964c00d8
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