Instructions to use ProbeX/Model-J__ResNet__model_idx_0938 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_0938 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_0938") 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_0938") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0938", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22e3a3a15a1bf21f4202be0831b53b6abf7b981184a448107c1b41ee7122cf9a
- Size of remote file:
- 5.37 kB
- SHA256:
- 0647793fb70a37622d1311b20a5c69f510a37c8e6e9e664ddd5ab5405145027c
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