Alexey

@WongKinYiu Hi, Yes, it seems VPU (Intel Myriad X) is highly optimized for Grouped-convolutional and may be SE-blocks. I will test it more. Maybe with new Google-Coral-TPU-edge in general, the...

@WongKinYiu Do you train ghostnet with CutMix+Mosaic+Label-smoothing? Also did we get improvement for any network with DropBlock?

@WongKinYiu I also added https://github.com/AlexeyAB/darknet/blob/master/cfg/efficientnet-lite3.cfg that you can try to train with `subdivisions=6` or `4`

@ShaneHsieh I added this information, so CPU uses FP32, VPU uses FP16, GPU uses FP32/16 (Tensor Cores). These devices use the lowest possible precision of floating point values ​​with increasing...

@andeyeluguo For using Yolo with OpenVINO (on CPU, GPU, VPU, ...) you should 1. install OpenVINO as usual 2. install OpenCV with OpenVINO-backend: https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend 3. run yolov3.cfg + yolov3.weights by...

@Luxonis-Brandon Current version of YOLOv4 is for Real-time on GPU. Later we will release YOLOv4-VPU for real-time >= 30 FPS on VPU.  ---- There are two ways to run...

@Luxonis-Brandon I just tested `csdarknet53-opt` (**YOLOv4 without MISH** in cfg set: `width=256 height=256` - 33.3% AP | 53.0% AP50) on your [DepthAI (Myriad X)](https://shop.luxonis.com/products/bw10980bc) device with network resolution 256x256 and...


YOLOv4-tiny released: https://github.com/AlexeyAB/darknet/issues/6067

But why is `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights -i 0 -thresh 0.25 --folder_inference ./result_img/` better than `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt >...