Instructions to use ProbeX/Model-J__ResNet__model_idx_0486 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_0486 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_0486") 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_0486") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0486", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26d93c181d858d21d33e51fcb8374a5bd2cd3760be890d293ce87f564801ad9f
- Size of remote file:
- 171 MB
- SHA256:
- c5be03734645d4d4913583ac00f39060dbecba4a5fc77bc9b87492c9019260c0
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