Instructions to use ProbeX/Model-J__ResNet__model_idx_0921 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_0921 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_0921") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0921") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0921", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0921: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0921/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0921/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0921/resolve/main/model.safetensors
171 MB
- Xet hash:
- 456dbbca8a0c836f6bed569878569c03c532844889ad6b21cb61979816be2854
- Size of remote file:
- 171 MB
- SHA256:
- ce3d4cc6023f58f9d30460f9fd4aecd53124a70d35d84d34ff52208443b45e53
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