yolov7-d2 is keeping involving. We have made some progress on exploring more light-weights model while still able get a favorable mAP. For example, we have made a MobileOne-S0 version model based on YOLOX-Lite achieves mAP 29.5, exceed YOLOX-Nano with faster speed on CPU.
We also achieves mAP 44 with a Convnexttiny model based on YOLOX arch.
But, we are still trying to expanding the model matrix and exploring more efficient && effective models, If you are interested in training or adding new model inside yolov7-d2, it's more than welcome!.
Roadmap of Next Half-Year 2022
CPU realtime Instance segmentation
this would need several steps:
[x] A fast and efficient detector as base;
[ ] An effective way to predict Masks;
[ ] Boost the mAP and accuracy, at least better than SOLOv2;
CPU realtime Pose estimation
[x] A baseline version;
[ ] Chasing KAPAO's result, get more accurate result
Replicate DINO's mAP
[ ] Replicate DINO's mAP, reconstruct whole code base in DINO;
[ ] Construct a more light-weighted transformer based model;
yolov7-d2 is keeping involving. We have made some progress on exploring more light-weights model while still able get a favorable mAP. For example, we have made a MobileOne-S0 version model based on YOLOX-Lite achieves mAP 29.5, exceed YOLOX-Nano with faster speed on CPU.
We also achieves mAP 44 with a Convnexttiny model based on YOLOX arch.
But, we are still trying to expanding the model matrix and exploring more efficient && effective models, If you are interested in training or adding new model inside yolov7-d2, it's more than welcome!.
Roadmap of Next Half-Year 2022
CPU realtime Instance segmentation
this would need several steps:
CPU realtime Pose estimation
Replicate DINO's mAP