2021年8月23日 · In YOLOv4, you will have the exact same ideas, but with: Binary cross entropy for the objectness and classification scores, Box-per-cell level prediction instead of cell level prediction for the class probabilities, so a slightly different penalization for the classification terms,

2020年9月13日 · I have trained a model of YOLOv4 by using my original dataset and the custom yolov4 configuration file, which I will refer to as my 'base' YOLOv4 model. Now I want to use this base model that I have created to train the model again using images that I have manually augmented. I am trying to retrain my models to try and increase the mAP and AP.

2021年11月11日 · What is the best way to run YOLOV4/YOLOV4-TINY on RPI 4 using Tensorflow-lite for object detection? I want to detect/count the no. of people in the room using this followed by detection of items like chair, banana e.t.c?

2021年2月6日 · I'm still new to "You Only Look Once" object detection algorithm (YOLOv4 to be exact). I have some questions regarding the mAP and loss chart. I tried to follow the instructions from AlexeyAB Darknet, and train my custom object detector using Google Colabs. After the training, it shows the loss and mAP chart as shown below. Loss and mAP chart:

2021年6月6日 · I'm trying to train a custom dataset using Darknet framework and Yolov4. I built up my own dataset but I get a Out of memory message in google colab. It also said "try to change subdivisions t...

2021年11月15日 · Also, for normal YoloV4 model I see the new .weights file in latest release (YOLOv4 16 days ago) but no new .cfg file, does it not need a new .cfg file and will work with old .cfg file from YOLOv4 pre-release (May 15, 2020) (I …

2021年2月4日 · What do all of these parameters from training YOLOv4 mean? (next mAP calculation at 1300 iterations) Last accuracy [email protected] = 63.16 %, best = 68.55 %. 1249: 26.351213, 24.018257 avg loss, 0.001000 rate, 2.983998 seconds, 39968 images, 10.505599 hours left Loaded: 0.000068 seconds

2020年11月30日 · Pytorch YOLOv4 (I am biased as I am a maintainer) has the ability to do this with darknet2pytorch. The following is an example snippet. from tool.darknet2pytorch import Darknet WEIGHTS = Darknet(cfgfile) WEIGHTS.load_weights(weightfile) Where cfgfile is your darknet config.cfg file, and weightfile is your darknet .wt weights.

Here is my current understanding of some of the variables. Not necessarily correct though: [net] batch: That many images+labels are used in the forward pass to compute a gradient and update the weights via backpropagation.

2021年6月23日 · I'm training my own datasets using Yolov4 from Alexeyab but i got a multiple bounding boxes like this image below. I googled and searched about NMS(non-maximum suppression) but all i can find is h...