Instructions to use ProbeX/Model-J__ResNet__model_idx_0944 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_0944 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_0944") 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_0944") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0944", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c20b16896f8c83bb418aaaa54b3dcc0dd8780d0c88c625e4baea0d6cbe946cb0
- Size of remote file:
- 5.37 kB
- SHA256:
- 6f94dec0950c02ba6a37bccc824a9ae1c7a5de910734dd565d30cb5232d770b2
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