Instructions to use ProbeX/Model-J__ResNet__model_idx_0469 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_0469 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_0469") 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_0469") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0469", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 266e4c53ce0628ff263a4f339acafe3db3b4fac8d2d81c44694ea9c481af8691
- Size of remote file:
- 171 MB
- SHA256:
- 16c309af5e0f06da3370a61c9da598137cdefc900a98f9e267ecb8b2b538b3be
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