Alexey

Results 266 comments of Alexey

I join a question. **1** Is there enough will get exactly the same way as an array as here? https://github.com/rbgirshick/caffe-fast-rcnn/blob/0dcd397b29507b8314e252e850518c5695efbb83/examples/cpp_classification/classification.cpp#L164 ``` Blob* output_layer = net_->output_blobs()[0]; const float* begin = output_layer->cpu_data();...

There seems to be inconsistency in detection using the Darknet and Tensorflow. TF detects fewer objects and with less confidence. It seems to find the reason you need a layer-by-layer...

What commands did you use to measure inference speed?

Which models did you compare? YOLOv7 - 161 FPS, 51.2% AP https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt and YOLOv5m6 - 90 FPS, 51.3% AP https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt ? Did you compare both models without NMS? What float...

I mean what command did you use to measure latency for Inference? So for YOLOv5m `45.4%` AP vs YOLOv7 `51.2%` AP you got: 60ms yolov5m vs 15ms yolov7 : Nvidia...

Try: `[-1, 1, nn.Upsample, [None, 4, 'nearest']],` https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L78 `[24, 1, Conv, [256, 1, 1]],` https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L79 `[11, 1, Conv, [128, 1, 1]],` https://github.com/WongKinYiu/yolov7/blob/main/cfg/training/yolov7.yaml#L93 ----

Try this cfg file: [yolov7_so.zip](https://github.com/WongKinYiu/yolov7/files/9286658/yolov7_so.zip) ---- I used ``` [-1, 1, nn.Upsample, [None, 4, 'nearest']], [11, 1, Conv, [128, 1, 1]], # route backbone P3 ``` instead of https://github.com/WongKinYiu/yolov7/blob/c14ba0c297b3b5fc0374c917db798c88f9dd226c/cfg/training/yolov7.yaml#L92-L93 ----...

I fixed and tested it. Try this cfg file: [yolov7_so.zip](https://github.com/WongKinYiu/yolov7/files/9289016/yolov7_so.zip) ---- I used ``` [-1, 1, nn.Upsample, [None, 4, 'nearest']], [11, 1, Conv, [128, 1, 1]], # route backbone P3...

> Thank you! With this modification, I get better results too, but not better than yolov5s6. Or just try yolov7-w6

Just use lower `--conf 0.10` or `--conf 0.025` and YOLOv7 will still have more Correct predictions and less Wrong predictions than YOLOv2/3/4/5/... Main issue, you are only looking for True...