Instructions to use ProbeX/Model-J__ResNet__model_idx_0787 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_0787 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_0787") 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_0787") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0787", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e514474151f67d950c0de1a801a1b1273a8b8733e2e186e999be310ce3b4e24a
- Size of remote file:
- 171 MB
- SHA256:
- 517059005606b6734eae80033eedab1f22b154d415da0c2f6f5cfbbac0d50c77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.