Instructions to use ProbeX/Model-J__ResNet__model_idx_0975 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_0975 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_0975") 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_0975") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0975", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a072b8e1ee771db4c35c624c99f8c7eda13a0247fe2270f9776b1fa1c30b70a
- Size of remote file:
- 171 MB
- SHA256:
- 1c9750dfb9512c74afe1ee8f49b1acd779d731b9bd16c0ab162208e4f9d77017
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.