From cd2bdec09030edf7da79ecdeb38d908c106850b3 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 23 Feb 2018 12:05:31 +0000
Subject: [PATCH] Updated to CUDA 9.1. And fixed no_gpu dependecies.
---
README.md | 54 ++++++++++++++++++++++++++++++++++++++----------------
1 files changed, 38 insertions(+), 16 deletions(-)
diff --git a/README.md b/README.md
index ebe9a1a..b7a4bd5 100644
--- a/README.md
+++ b/README.md
@@ -8,10 +8,11 @@
4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
6. [When should I stop training](#when-should-i-stop-training)
-7. [How to improve object detection](#how-to-improve-object-detection)
-8. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
-9. [Using Yolo9000](#using-yolo9000)
-10. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
+7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007)
+8. [How to improve object detection](#how-to-improve-object-detection)
+9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
+10. [Using Yolo9000](#using-yolo9000)
+11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
|  |  https://arxiv.org/abs/1612.08242 |
|---|---|
@@ -31,13 +32,13 @@
* both Windows and Linux
* both OpenCV 3.x and OpenCV 2.4.13
-* both cuDNN 5 and cuDNN 6
+* both cuDNN v5-v7
* CUDA >= 7.5
* also create SO-library on Linux and DLL-library on Windows
##### Requires:
* **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409))
-* **CUDA 8.0**: https://developer.nvidia.com/cuda-downloads
+* **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads
* **OpenCV 3.x**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.exe/download
* **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download
- OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi`
@@ -116,7 +117,7 @@
Just do `make` in the darknet directory.
Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
* `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`)
-* `CUDNN=1` to build with cuDNN v5/v6 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
+* `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
* `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams
* `DEBUG=1` to bould debug version of Yolo
* `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU
@@ -141,7 +142,7 @@
5. If you want to build with CUDNN to speed up then:
- * download and install **cuDNN 6.0 for CUDA 8.0**: https://developer.nvidia.com/cudnn
+ * download and install **cuDNN 7.0 for CUDA 9.1**: https://developer.nvidia.com/cudnn
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
@@ -309,23 +310,42 @@
2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
-* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
-* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights`
-* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights`
+(If you use another GitHub repository, then use `darknet.exe detector recall`... instead of `darknet.exe detector map`...)
+
+* `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
+* `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):
-> 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00%
-
-* **IOU** - the bigger, the better (says about accuracy) - **better to use**
-* **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used
+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**.
+Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
+
+* **IoU** (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24
+
+* **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf
+
+In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but **IoU always has the same meaning**.
-How to calculate **mAP** [voc_eval.py](https://github.com/AlexeyAB/darknet/blob/master/scripts/voc_eval.py) or [datascience.stackexchange link](https://datascience.stackexchange.com/questions/16797/what-does-the-notation-map-5-95-mean)
+### How to calculate mAP on PascalVOC 2007:
+
+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
+* 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`
+* 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:
+ 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%
+ 2. Using this fork of Darknet: run the file `build/darknet/x64/calc_mAP.cmd` - you will get mAP for `yolo-voc.cfg` model, mAP = 75.8%
+
+ (The article specifies the value of mAP = 76.8% for YOLOv2 416×416, page-4 table-3: https://arxiv.org/pdf/1612.08242v1.pdf. We get values lower - perhaps due to the fact that the model was trained on a slightly different source code than the code on which the detection is was done)
+
+* if you want to get mAP for `tiny-yolo-voc.cfg` model, then un-comment line for tiny-yolo-voc.cfg and comment line for yolo-voc.cfg in the .cmd-file
+* if you have Python 2.x instead of Python 3.x, and if you use Darknet+Python-way to get mAP, then in your cmd-file use `reval_voc.py` and `voc_eval.py` instead of `reval_voc_py3.py` and `voc_eval_py3.py` from this directory: https://github.com/AlexeyAB/darknet/tree/master/scripts
### Custom object detection:
@@ -343,6 +363,8 @@
* for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
+ * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
+
2. After training - for detection:
* Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4)
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