Instructions to use ProbeX/Model-J__ResNet__model_idx_0307 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_0307 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_0307") 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_0307") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0307", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2d89d0dc373baaf56c33e7d36617d8e32d1c1a5433bdbb54c78cdd09839b365
- Size of remote file:
- 171 MB
- SHA256:
- 8d89e81d0321797d9058ffcf40bb6ab7ab69ff7a208efb2c8674b3d4ee332687
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.