Instructions to use Roboflow/rf-detr-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roboflow/rf-detr-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Roboflow/rf-detr-segmentation")# Load model directly from transformers import AutoImageProcessor, RfDetrForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("Roboflow/rf-detr-segmentation") model = RfDetrForInstanceSegmentation.from_pretrained("Roboflow/rf-detr-segmentation", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Int8 quantization for RF-DETR seg support?
#2
by nvriese1 - opened
Question in title, are there full int8 quantized safetensors / pth, tensorrt (.engine) weights available? If not, is there documentation for how to quantize from fp32 to int8 from the seg (not object detection) weights via the rfdetr package or similar?
A standard full int8 quant via trtexec yields a non-performant graph due to the presence of precision sensitive layers.