Instructions to use ProbeX/Model-J__ResNet__model_idx_0505 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_0505 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_0505") 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_0505") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0505", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a45a107f8c2a54758f341e9c74ec11ec4d7b1125f85a17846bbf88fd78d86f72
- Size of remote file:
- 171 MB
- SHA256:
- 5f434260aaab4d60afe9632b8dcb30bc4fc58aaf4b1e67b30df9000db979a010
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