Instructions to use ProbeX/Model-J__ResNet__model_idx_0095 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_0095 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_0095") 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_0095") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0095", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45d24012722db094dcd695ef8b5996bc21d7860261ac969dd0efd702fb2130fb
- Size of remote file:
- 5.37 kB
- SHA256:
- b4fadfcd682ed66b9512300aa88bd9483fa25c4b09b1e39b5daccaa7d09c716f
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