Instructions to use ProbeX/Model-J__ResNet__model_idx_0607 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_0607 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_0607") 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_0607") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0607", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0607: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0607/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0607/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0607/resolve/main/model.safetensors
171 MB
- Xet hash:
- 9ef71c21c30bbec72a8315b5ee644fa3981244134a0c241fe0a604154ff6774e
- Size of remote file:
- 171 MB
- SHA256:
- 2812b44f4a047e1af95aac1141d587028ba1f31b545bbdf8f946e948a983092f
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