Instructions to use ProbeX/Model-J__ResNet__model_idx_0919 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_0919 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_0919") 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_0919") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0919", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1a144beefd0d3e272144db580bca672696182f78b5007d4406e8faacfc576a1
- Size of remote file:
- 171 MB
- SHA256:
- 11cb7450137c0c0b2a5d829873682249c4ea7cddccfe76535aaa38609d70387a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.