Instructions to use ProbeX/Model-J__ResNet__model_idx_0715 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_0715 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_0715") 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_0715") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0715", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7fcfbc0b62027b11297213899cf741c43f2b230d7b9f3f4fc5e8b92c2f9fe93
- Size of remote file:
- 171 MB
- SHA256:
- d06004a4263bd23be89520c2d219289c15937e374bec01fe391b56f115ba2cb1
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