forked from pjreddie/darknet
-
Notifications
You must be signed in to change notification settings - Fork 8k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1 changed file
with
32 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -41,16 +41,46 @@ More details: http://pjreddie.com/darknet/yolo/ | |
|
||
|
||
|
||
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) | ![map_fps](https://user-images.githubusercontent.com/4096485/80163662-7ed04100-85df-11ea-8db7-1232b1158827.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | | ||
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) | ![readme](https://user-images.githubusercontent.com/4096485/80213782-5f1e3480-8642-11ea-8fdf-0e6b9a6b5f4c.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | | ||
|---|---| | ||
|
||
* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80163825-061db480-85e0-11ea-9ff9-13c7143789cb.png) | ||
* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80213824-6e9d7d80-8642-11ea-94a6-0be90c7d7cd5.png) | ||
* CSPNet: [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) [paper](https://arxiv.org/abs/1911.11929) Comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks | ||
* Yolo v3 on MS COCO: [Speed / Accuracy ([email protected]) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) | ||
* Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf | ||
* Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg | ||
* Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg | ||
|
||
#### How to evaluate AP of YOLOv4 on the MS COCO evaluation server | ||
|
||
1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip | ||
2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt | ||
3. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT | ||
4. Content of the file `cfg/coco.data` should be | ||
``` | ||
classes= 80 | ||
train = <replace with your path>/trainvalno5k.txt | ||
valid = <replace with your path>/testdev2017.txt | ||
names = data/coco.names | ||
backup = backup | ||
eval=coco | ||
``` | ||
5. Create `/results/` folder near with `./darknet` executable file | ||
6. Run validation: `./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights` | ||
7. Rename the file `/results/coco_results.json` to `detections_test-dev2017_yolov4_results.json` and compress it to `detections_test-dev2017_yolov4_results.zip` | ||
8. Submit file `detections_test-dev2017_yolov4_results.zip` to the MS COCO evaluation server for the `test-dev2019 (bbox)` | ||
|
||
#### How to evaluate FPS of YOLOv4 on GPU | ||
|
||
1. Compile Darknet with `GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1` in the `Makefile` (or use the same settings with Cmake) | ||
2. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT | ||
3. Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance) | ||
4. Run one of two commands and look at the AVG FPS: | ||
* include video_capturing + NMS + drawing_bboxes: | ||
`./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output` | ||
* exclude video_capturing + NMS + drawing_bboxes: | ||
`./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark` | ||
|
||
#### Pre-trained models | ||
|
||
There are weights-file for different cfg-files (trained for MS COCO dataset): | ||
|