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@ -15,12 +15,14 @@
10. [Using Yolo9000](#using-yolo9000) 10. [Using Yolo9000](#using-yolo9000)
11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) 11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg) https://arxiv.org/abs/1612.08242 |
|---|---|
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg) https://arxiv.org/abs/1612.08242 |
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) https://pjreddie.com/media/files/papers/YOLOv3.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
# "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" # "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)"
A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
@ -46,11 +48,11 @@ This repository supports:
* **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
* `yolov3.cfg` (236 MB COCO-model **v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights * `yolov3.cfg` (236 MB COCO **Yolo v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights
* `yolov2.cfg` (194 MB COCO-model v2) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights * `yolov2.cfg` (194 MB COCO Yolo v2) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights
* `yolo-voc.cfg` (194 MB VOC-model v2) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights * `yolo-voc.cfg` (194 MB VOC Yolo v2) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
* `yolov2-tiny.cfg` (43 MB COCO-model v2) - require 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - require 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights
* `yolov2-tiny-voc.cfg` (60 MB VOC-model v2) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
* `yolo9000.cfg` (186 MB Yolo9000-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights * `yolo9000.cfg` (186 MB Yolo9000-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
Put it near compiled: darknet.exe Put it near compiled: darknet.exe
@ -355,7 +357,7 @@ In terms of Wiki, indicators Precision and Recall have a slightly different mean
1. To calculate mAP (mean average precision) on PascalVOC-2007-test: 1. To calculate mAP (mean average precision) on PascalVOC-2007-test:
* Download PascalVOC dataset, install Python 3.x and get file `2007_test.txt` as described here: https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data * Download PascalVOC dataset, install Python 3.x and get file `2007_test.txt` as described here: https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data
* Then download file https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/voc_label_difficult.py to the dir `build\darknet\x64\data\voc` then run `voc_label_difficult.py` to get the file `difficult_2007_test.txt` * Then download file https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/voc_label_difficult.py to the dir `build\darknet\x64\data\` then run `voc_label_difficult.py` to get the file `difficult_2007_test.txt`
* Remove symbol `#` from this line to un-comment it: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/voc.data#L4 * Remove symbol `#` from this line to un-comment it: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/voc.data#L4
* Then there are 2 ways to get mAP: * Then there are 2 ways to get mAP:
1. Using Darknet + Python: run the file `build/darknet/x64/calc_mAP_voc_py.cmd` - you will get mAP for `yolo-voc.cfg` model, mAP = 75.9% 1. Using Darknet + Python: run the file `build/darknet/x64/calc_mAP_voc_py.cmd` - you will get mAP for `yolo-voc.cfg` model, mAP = 75.9%

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