Instructions to use ProbeX/Model-J__ResNet__model_idx_0544 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_0544 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_0544") 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_0544") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0544", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66a598951d94946119235f56bc219fa3a936cfd62b2c40bca98a7f6c02cfb6e6
- Size of remote file:
- 5.37 kB
- SHA256:
- 175e696a719e42a5dcaa78f9ce498ece8e955a9cd7465608f5d7d8439a9add4c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.