Instructions to use ProbeX/Model-J__ResNet__model_idx_0039 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_0039 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_0039") 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_0039") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0039", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0039: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0039/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0039/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0039/resolve/main/model.safetensors
171 MB
- Xet hash:
- a6e182034a5481576d461b07e8526c5e0e85c0a6d78ee5a2424e6ba8562d8ffe
- Size of remote file:
- 171 MB
- SHA256:
- 255bcdec087fd440dc8f29cf3e90a24bc41dc7ded3add988fd303f11e0a05a1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.