Instructions to use ProbeX/Model-J__ResNet__model_idx_0784 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_0784 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_0784") 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_0784") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0784", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c442e9a387e809bf3ac82c5a66a05fd9484a457e58d20ea9a347e189a682168
- Size of remote file:
- 171 MB
- SHA256:
- 224e227c71cd5fd7e299d09be55cf8229d90650919fe0dccac161a3d187436f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.