Instructions to use Eshwar14/my_resnet_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eshwar14/my_resnet_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Eshwar14/my_resnet_model")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Eshwar14/my_resnet_model") model = AutoModelForObjectDetection.from_pretrained("Eshwar14/my_resnet_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94331b1c71bc59a726e9d01b8af0a4a1ea6d8bab03c4ada1cc50102741844cd7
- Size of remote file:
- 167 MB
- SHA256:
- 12ea9f0c5aa32719d9723500c356fd6262ecf27e070ff7fde68b4380f449bc94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.