Instructions to use Bolishetti/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bolishetti/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Bolishetti/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Bolishetti/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("Bolishetti/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Bolishetti/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Bolishetti/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Bolishetti/detr-resnet-50_finetuned_cppe5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Bolishetti/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- ae34d9ee1e00e0e8fb6386e66bddd893eb541874ddd1211d92ccc75b23b90ad4
- Size of remote file:
- 167 MB
- SHA256:
- b5b515872fbf035523e5de8077c46aca3307d10e34e7108bec20aec47fec17be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.