Also the MSE w-h loss tended to be unstable, which is solved by GIoU. darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights And comapre last output lines for each weights (7000, 8000, 9000): Choose weights-file with the highest IoU (intersect of union) and mAP (mean average precision) For example, bigger IOU gives weights yolo-obj_8000.weights - then use this weights for detection. Since I found that nms_kind=greedynms beta1=0.6 is not in Gaussian_yolov3_BDD.cfg(this repo),but in coco-ciou.cfg(DIoU-darknet repo) privacy statement. to your account. Thanks a bunch!

darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights; darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights; And comapre last output lines for each weights (7000, 8000, 9000): Choose weights-file with the highest IoU (intersect of union) and mAP (mean average precision) For example, bigger IOU gives weights yolo-obj_8000.weights - then use this … Darknet: Open Source Neural Networks in C. Darknet is an open source neural network framework written in C and CUDA. Sign in

Also try to set 11 pr-points instead of 101 points in both Darknet and Pycocotool, for easier debugging. Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

So add the same to every gaussian_yolo? We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Hi @AlexeyAB , I know you are very busy but could you please consider my issue. 重新计算你的数据集的anchor:(注意设置的时候计算问题) darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416 darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights And comapre last output lines for each weights (7000, 8000, 9000): Choose weights-file with the highest IoU (intersect of union) and mAP (mean average precision) Or if you train with flag -map then you will see mAP indicator Last accuracy mAP@0.5 = 18.50% in the console - this indicator is better than Loss, so train while mAP increases. However, for mAP@0.75, I'm getting really low which is about 14% only. I'm training YOLOv4 on custom dataset and obtaining a high mAP@0.5 = ~95%. You should run detector test command with flag -thresh 0.001 as it is done for mAP-calculation by default: There is solved this issue: #2140 (comment).

Any idea why this might happen?

before fine-tuning. Learn more.

上图是我训练自己的模型生成的。 展开阅读全文 点赞 2; 评论 5; 分享. classes=1000 train = data/imagenet1k.train_c.list valid = data/inet.val_c.list backup = backup labels = … 23 Apr 2020

@nyj-ocean Yes you can. Do you have any further thoughts @AlexeyAB ? We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Successfully merging a pull request may close this issue. You signed in with another tab or window. I'm training YOLOv4 on custom dataset and obtaining a high mAP@0.5 = ~95%.

You signed in with another tab or window. I want to know these improvement in mAP and Recall of yolov3+Gaussian+CIoU are just for random or for the added module Gaussian+CIoU?



set iou_normalizer=1 for [yolo] Successfully merging a pull request may close this issue. Different approaches of mAP (mean average precision) calculation:-map_points 101 for MS COCO-map_points 11 for PascalVOC 2007 (uncomment difficult in voc.data)-map_points 0 for ImageNet, PascalVOC 2010-2012 and your custom dataset; For example: use this command to calculate mAP@0.5 for ImageNet, PascalVOC 2010-2012 and your custom dataset: ./darknet detector map cfg/coco.data …

It does not compute a gradient, it simply mutates successive generations based on combinations of the most successful parents. I'm training YOLOv4 on custom dataset and obtaining a high mAP@0.5 = ~95%. I abandoned the MSE loss a while ago, as I had problems balancing the x-y losses with the width-height losses, since their loss functions are quite different.

At this time it is calculated every 300 iterations which is too frequent for me.

I have only few gpus, I think I can not afford the computation of training hyper-parameter searching.

Furthermore, on the COCO dataset [14], the AP of Gaussian YOLOv3 is 36.1, which is 3.1 higher than YOLOv3.

提升分辨率：416--> 608等必须是32倍数.

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.

they're used to gather information about the pages you visit and how many clicks you need to accomplish a task.

Ranked #3 on Thanks a bunch! So there are 3 thresholds in the ./darknet detector map: thresh_calc_avg_iou = 0.25 - confidence-threshold is used only for: F1, FN/FP/TP, Precision, Recall, IoU, iou_thresh = 0.5 - IoU-threshold for mAP calculation (mAP@0.5 by default), thresh = 0.005 - confidence-threshold for mAP to remove the most badly detections, Also you can calculate mAP on MS COCO test-dev evaluation server: #2145 (comment).

to your account. Hyperparameter evolution is run the same as the training command, except with the --evolve flag included. I found original yolov3 and Gaussian yolo gave me almost the same mAP after more after around 35000 iterations for 54 class problem.

@AlexeyAB

BDD100k weights file: https://drive.google.com/open?id=1Eutnens-3z6o4LYe0PZXJ1VYNwcZ6-2Y. Based on the predicted output, it seems that the bounding box is way too big compared to ground truth, but it does sort of being able to detect the location correctly (refer image below). Successfully merging a pull request may close this issue.

I want to know these improvement in mAP and Recall of yolov3+Gaussian+CIoU are just for random or for the added module Gaussian+CIoU? Ranked #6 on https://github.com/ultralytics/yolov3#map, https://lambdalabs.com/deep-learning/workstations/4-gpu/basic/customize, Test models with good hyperparameters - +4.8% mAP@0.5 on MS COCO test-dev. Both Training, Validation and Test datasets must have txt-labels files with ground-truth boxes.

•. My data was one-class dataset with 130k images, but for some reason there no validation data, so I can't report the mAP value. This is object detection by new method

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