- 'darknet53-coco' — A pretrained YOLO v3 deep learning network created using DarkNet-53 as the base network and trained on COCO dataset.
- 'tiny-yolov3-coco' — A pretrained YOLO v3 deep learning network created using a small base network and trained on COCO dataset.
Yolo v3 training on coco data set
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Hi,
I want to train the the yolo v3 model with coco dataset. how can i do that?
Thanks
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回答 (3 件)
T.Nikhil kumar
2022 年 7 月 9 日
Hey Asif !
I understand that you want to build a yolov3 object detector model and train it on the COCO dataset.
There are pretrained YOLOv3 object detectors trained on COCO dataset. You do not need to train a network separately. The following command lets you create a detector using YOLO v3 deep learning networks trained on a COCO dataset.
Here, name is the name of the pretrained YOLO v3 deep learning network, specified as one of these:
For reference ,please go through
If you still wish to perform training on your own then please refer the following example
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Divya Gaddipati
2020 年 7 月 22 日
You can refer to the following link for training a YOLOv3 object detector. In place of the dataset used in this example, you can load your own dataset and arrange it in the same format as described in the example
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cui,xingxing
2020 年 8 月 19 日
This official example cellfun function is not recommended, and it is better to support custom building yolov3Layer.
cui,xingxing
2020 年 8 月 19 日
編集済み: cui,xingxing
2024 年 4 月 27 日
This is the yolov3 you want, but there is a problem with saving the model during training, especially the parameter saving of the bn layer should be consistent with darknet, and the labeled [x, y, w, h], instead of Normalized [center_x, center_y, w, h ]
-------------------------Off-topic interlude, 2024-------------------------------
I am currently looking for a job in the field of CV algorithm development, based in Shenzhen, Guangdong, China,or a remote support position. I would be very grateful if anyone is willing to offer me a job or make a recommendation. My preliminary resume can be found at: https://cuixing158.github.io/about/ . Thank you!
Email: cuixingxing150@gmail.com
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