:zap: Based on yolo's ultra-lightweight universal target detection algorithm, the calculation amount is only 250mflops, the ncnn model size is only 666kb, the Raspberry Pi 3b can run up to 15fps+, and the mobile terminal can run up to 178fps+
Really interesting work. I was wondering where can I find a simple architecture definition for Yolo-Fastest or Yolo-Fastest-XL? I am asking because I wanted to port it to TensorFlow to compare it with my own work.
Also, if you can provide more details on the training method, like which data augmentations, loss functions etc. you use, it would also be an interesting study.
Even if you are not releasing a paper, a simple doc with such details would be really helpful for someone following up on your work. For example, what exactly is your backbone? Where do you cut it off and what feature aggregation method did you use? Also how many object detection heads does your model have? These details would be really helpful.
Hi,
Really interesting work. I was wondering where can I find a simple architecture definition for Yolo-Fastest or Yolo-Fastest-XL? I am asking because I wanted to port it to TensorFlow to compare it with my own work.
Also, if you can provide more details on the training method, like which data augmentations, loss functions etc. you use, it would also be an interesting study.
Even if you are not releasing a paper, a simple doc with such details would be really helpful for someone following up on your work. For example, what exactly is your backbone? Where do you cut it off and what feature aggregation method did you use? Also how many object detection heads does your model have? These details would be really helpful.
Thank You