Instructions to use ProbeX/Model-J__ResNet__model_idx_0323 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_0323 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_0323") 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_0323") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0323", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7aee3bfe7b3518e79e489ebfc1224e23c823d163091d0b7a0b7c3e1508525b2c
- Size of remote file:
- 171 MB
- SHA256:
- fbda30b7f184dceeddb70368f54214235d6c7ebd0e120dec7787059eb1fae680
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