Instructions to use ProbeX/Model-J__ResNet__model_idx_0215 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_0215 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_0215") 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_0215") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0215", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b0842594daad3ab299675b00863bb2c2800f9a1cbad0465136140d45f01260b
- Size of remote file:
- 5.37 kB
- SHA256:
- 8525f323728b653887a610a9b11db3fb371d78d3273d3b97ae7423f1acedb7f1
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