Instructions to use ProbeX/Model-J__ResNet__model_idx_0535 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_0535 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_0535") 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_0535") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0535") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a30fae259ec3529a45b16f96e1870b9058ac1c9c1e711131355815dc8a10cdf8
- Size of remote file:
- 5.37 kB
- SHA256:
- 29223bc5ac4cd63e7929a0509c45d6230752b31fd776526b9e2aaca04a872e59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.