Instructions to use ProbeX/Model-J__ResNet__model_idx_0063 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_0063 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_0063") 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_0063") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0063", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30ebf98773ee7e60eb5dff334ead1739d1ac2dc22acddc284c3f3cb8a64f9b95
- Size of remote file:
- 5.37 kB
- SHA256:
- 72ea9c9732dbc79dbacaaf52cadcb34cf09a9c292ff3e987decc43749282f85b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.