Instructions to use ProbeX/Model-J__ResNet__model_idx_0303 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_0303 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_0303") 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_0303") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0303", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 420a9a2f26bc336efcde20da3f95f1af5295924deb75d3a73b9a3abe604c38f0
- Size of remote file:
- 5.37 kB
- SHA256:
- 479e2c5faa62f924539f172b00aafec626a8cb783303c2b4f8354ebc732b357a
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