Instructions to use ProbeX/Model-J__ResNet__model_idx_0202 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_0202 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_0202") 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_0202") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0202", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0202: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0202/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0202/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0202/resolve/main/model.safetensors
171 MB
- Xet hash:
- f9b34e0abc784b136ccd153a82469e5d3345179a1526016dabdca07af2cfce3b
- Size of remote file:
- 171 MB
- SHA256:
- 7e847526289e16b841fc7cb40a8a00f3c761a0c8d5a9c18c025ed928f2b324d3
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