Instructions to use ProbeX/Model-J__ResNet__model_idx_0923 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_0923 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_0923") 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_0923") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0923", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f7f408e13f8d18c559474ee8bb88fed041678a2c6ed5c0fb31f7bd22a60c03f
- Size of remote file:
- 171 MB
- SHA256:
- a26336e9b31eb8dac889ca4d5dea10f11f9fb0128e39828eebd29ef170131c1a
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