Instructions to use ProbeX/Model-J__ResNet__model_idx_0925 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_0925 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_0925") 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_0925") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0925", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8d8656a83d16f279dff5ca23bf543102dcab107e2656c01785e8a33bb572148
- Size of remote file:
- 171 MB
- SHA256:
- afa73b36d8e237c8334dcb574c91d69f303e0bf705a99ff51dbed7fae7df5fb1
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