Instructions to use ProbeX/Model-J__ResNet__model_idx_0959 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_0959 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_0959") 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_0959") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0959", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0959: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0959/resolve/3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0959@3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0959/resolve/3f7302506a35f4e5f062aa4ed69a9e9d4e4ce371/model.safetensors
171 MB
- Xet hash:
- f4bdce7c30c8208220742b81291572cf0fa68c22f460faeebeef3659da27359c
- Size of remote file:
- 171 MB
- SHA256:
- b9cb35cf850f18552bf83004da76772a6bc7558350af5dc63ee95ec0bb106457
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