YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the COCO dataset.
This is based on the implementation of Yolo-v7-Quantized found here. This repository contains scripts for optimized on-device export suitable to run on Qualcomm® devices. More details on model performance accross various devices, can be found here.
Sign up to start using Qualcomm AI Hub and run these models on a hosted Qualcomm® device.
Install the package via pip:
pip install "qai_hub_models[yolov7_quantized]"Once installed, run the following simple CLI demo:
python -m qai_hub_models.models.yolov7_quantized.demoMore details on the CLI tool can be found with the --help option. See
demo.py for sample usage of the model including pre/post processing
scripts. Please refer to our general instructions on using
models for more usage instructions.
This repository contains export scripts that produce a model optimized for on-device deployment. This can be run as follows:
python -m qai_hub_models.models.yolov7_quantized.exportAdditional options are documented with the --help option. Note that the above
script requires access to Deployment instructions for Qualcomm® AI Hub.
- The license for the original implementation of Yolo-v7-Quantized can be found here.
- The license for the compiled assets for on-device deployment can be found here
- YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
- Source Model Implementation
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.