Instructions to use ProbeX/Model-J__ResNet__model_idx_0064 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_0064 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_0064") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0064") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0064", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc1095d76a41764d82572e9adc1c1406882d928e81679063aeb8c58f30598b60
- Size of remote file:
- 171 MB
- SHA256:
- 5792ce573ccea914ce4bc5b949a1249dcae0e6f11f20846cfdb1328b3e17b62d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.