Instructions to use ProbeX/Model-J__ResNet__model_idx_0958 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_0958 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_0958") 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_0958") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0958") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4af0a4d9116f1f62e1671ccf21fdc97de5212ea4d48ee48f17cde310310e84a
- Size of remote file:
- 5.37 kB
- SHA256:
- 8e965a67720746652878476a907314c1c829cec696ec755cf5fc9e2e706e6272
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.