Instructions to use ProbeX/Model-J__ResNet__model_idx_0692 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_0692 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_0692") 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_0692") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0692", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0692: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0692/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0692/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0692/resolve/main/model.safetensors
171 MB
- Xet hash:
- 95508d2c171605a3bcd9b74048c4e60d40e3c6c85c20e60fd9fea511309337b5
- Size of remote file:
- 171 MB
- SHA256:
- a927d85e983fc6ea1e56a7ead99e4625d5d0454172ea08861ac3bdb9ff976abc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.