Instructions to use decene/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use decene/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="decene/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("decene/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("decene/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from decene/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/decene/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://decene/detr-resnet-50_finetuned_cppe5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/decene/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 1b52f42daf5f03288a74e6e6b3bd3cd9df1eaa04a8ff5bb210c79e51a3532050
- Size of remote file:
- 167 MB
- SHA256:
- aeabb8609a5b463ca915be3eab3af241a0798a8712279b3bd302db5a4c8d4f7a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.