darknet训练自身数据集的小问题

1.首先看数据集是否有非jpg格式图片

2.sub_batch = batch/subdivision, 实际在每个sub_batch后先不迭代，等整个batch计算完之后迭代一次。降低显存要求

3.起初测试集的置信度阈值设置太低0.25，导致最后有些低置信度阈值的box（比如0.25,0.37）没有被抑制掉。一般设置到0.5左右。

4.控制台命令行训练代码理解(控制台命令行调用darknet框架，同理调用其他)：

darknet.exe detector train data/img.data yolo-obj.cfg darknet53.conv.74 -map

首先darknet.exe找到函数执行入口，相当于找到main函数的位置。

后面是形参列表（argc,argv） argv[0]是当前进程的完整执行路径，arg[1] = = detector，darknet的main函数里有strmp(argv[1]，"detector") == 0,则执行函数体run_detector(argc, argv)，进入run_detector函数，进行第二个参数判断argv[2]，如训练argv[2] = = train，则执行函数 train_detector()，

如darknet.exe detector train data/img.data yolo-obj.cfg darknet53.conv.74 -map

data/img.data == datacfg

yolo-obj.cfg == cfg

darknet53.conv.74 == weights

-map == calc_map

1 int calc_map = find_arg(argc, argv, "-map");
2 if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box);
3     else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs);
4     else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
5     else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
6     else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL);
7     else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);

有calc_map会进入以下函数体，做mAP计算。此处可以修改计算一次mAP的迭代次数。修改——重新编译——再训练。

1 int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochs
2         calc_map_for_each = fmax(calc_map_for_each, 100);
3         int next_map_calc = iter_map + calc_map_for_each;
4         next_map_calc = fmax(next_map_calc, net.burn_in);
5         next_map_calc = fmax(next_map_calc, 400);
6         if (calc_map) {
7             printf("
 (next mAP calculation at %d iterations) ", next_map_calc);
8             if (mean_average_precision > 0) printf("
 Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100);
9         }

5.burn in参数

框架将burn in次前的迭代时，学习率更新策略为从小到大；之后的为递减。

小样本要将burn in 适当调小，加快收敛。大概为imgs/batch_size的10—20倍