Instructions to use ProbeX/Model-J__ResNet__model_idx_0132 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_0132 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_0132") 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_0132") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0132", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad9345a628425f574eb87c7f7a52527600fecaa0bb62a581f9c659043f74fb6d
- Size of remote file:
- 171 MB
- SHA256:
- 06e05d687eba448c8149fec13bcca08ee82a6bdb922be5790a0b68ca5739ffa1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.