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yolov5中anchors设置实例详解

作者:高祥xiang

在YOLOV5算法之中,针对不同的数据集,一般会预先设置固定的Anchor,下面这篇文章主要给大家介绍了关于yolov5中anchors设置的相关资料,文中通过实例代码介绍的非常详细,需要的朋友可以参考下

yolov5中增加了自适应锚定框(Auto Learning Bounding Box Anchors),而其他yolo系列是没有的。

一、默认锚定框

Yolov5 中默认保存了一些针对 coco数据集的预设锚定框,在 yolov5 的配置文件*.yaml 中已经预设了640×640图像大小下锚定框的尺寸(以 yolov5s.yaml 为例):

# anchors
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

 anchors参数共有三行,每行9个数值;且每一行代表应用不同的特征图;

1、第一行是在最大的特征图上的锚框

2、第二行是在中间的特征图上的锚框

3、第三行是在最小的特征图上的锚框;

在目标检测任务中,一般希望在大的特征图上去检测小目标,因为大特征图才含有更多小目标信息,因此大特征图上的anchor数值通常设置为小数值,而小特征图上数值设置为大数值检测大的目标。

二、自定义锚定框

1、训练时自动计算锚定框

yolov5 中不是只使用默认锚定框,在开始训练之前会对数据集中标注信息进行核查,计算此数据集标注信息针对默认锚定框的最佳召回率,当最佳召回率大于或等于0.98,则不需要更新锚定框;如果最佳召回率小于0.98,则需要重新计算符合此数据集的锚定框。

核查锚定框是否适合要求的函数在 /utils/autoanchor.py 文件中:

def check_anchors(dataset, model, thr=4.0, imgsz=640):

 其中 thr 是指 数据集中标注框宽高比最大阈值,默认是使用 超参文件 hyp.scratch.yaml 中的 “anchor_t” 参数值。

核查主要代码如下:

    def metric(k):  # compute metric
        r = wh[:, None] / k[None]
        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
        best = x.max(1)[0]  # best_x
        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
        bpr = (best > 1. / thr).float().mean()  # best possible recall
        return bpr, aat
 
    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))

其中两个指标需要解释一下(bpr 和 aat):

bpr(best possible recall) 

aat(anchors above threshold) 

 其中 bpr 参数就是判断是否需要重新计算锚定框的依据(是否小于 0.98)。

重新计算符合此数据集标注框的锚定框,是利用 kmean聚类方法实现的,代码在  /utils/autoanchor.py 文件中:

def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
    """ Creates kmeans-evolved anchors from training dataset
        Arguments:
            path: path to dataset *.yaml, or a loaded dataset
            n: number of anchors
            img_size: image size used for training
            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
            gen: generations to evolve anchors using genetic algorithm
            verbose: print all results
        Return:
            k: kmeans evolved anchors
        Usage:
            from utils.autoanchor import *; _ = kmean_anchors()
    """
    thr = 1. / thr
    prefix = colorstr('autoanchor: ')
 
    def metric(k, wh):  # compute metrics
        r = wh[:, None] / k[None]
        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
        # x = wh_iou(wh, torch.tensor(k))  # iou metric
        return x, x.max(1)[0]  # x, best_x
 
    def anchor_fitness(k):  # mutation fitness
        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
        return (best * (best > thr).float()).mean()  # fitness
 
    def print_results(k):
        k = k[np.argsort(k.prod(1))]  # sort small to large
        x, best = metric(k, wh0)
        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
        for i, x in enumerate(k):
            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
        return k
 
    if isinstance(path, str):  # *.yaml file
        with open(path) as f:
            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
        from utils.datasets import LoadImagesAndLabels
        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
    else:
        dataset = path  # dataset
 
    # Get label wh
    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
 
    # Filter
    i = (wh0 < 3.0).any(1).sum()
    if i:
        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
 
    # Kmeans calculation
    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
    s = wh.std(0)  # sigmas for whitening
    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
    k *= s
    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
    k = print_results(k)
 
    # Plot
    # k, d = [None] * 20, [None] * 20
    # for i in tqdm(range(1, 21)):
    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
    # ax = ax.ravel()
    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
    # fig.savefig('wh.png', dpi=200)
 
    # Evolve
    npr = np.random
    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
    for _ in pbar:
        v = np.ones(sh)
        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
        kg = (k.copy() * v).clip(min=2.0)
        fg = anchor_fitness(kg)
        if fg > f:
            f, k = fg, kg.copy()
            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
            if verbose:
                print_results(k)
 
    return print_results(k)

对 kmean_anchors()函数中的参数做一下简单解释(代码中已经有了英文注释):

如果你不想自动计算锚定框,可以在 train.py 中设置参数即可:

parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')

2、训练前手动计算锚定框

如果使用 yolov5 训练效果并不好(排除其他原因,只考虑 “预设锚定框” 这个因素), yolov5在核查默认锚定框是否符合要求时,计算的最佳召回率大于0.98,没有自动计算锚定框;此时你可以自己手动计算锚定框。【即使自己的数据集中目标宽高比最大值小于4,默认锚定框也不一定是最合适的】

 首先可以自行编写一个程序,统计一下你所训练的数据集所有标签框宽高比,看下宽高比主要分布在哪个范围、最大宽高比是多少? 比如:你使用的数据集中目标宽高比最大达到了 5:1(甚至 10:1) ,那肯定需要重新计算锚定框了,针对coco数据集的最大宽高比是 4:1 。

然后在 yolov5 程序中创建一个新的 python 文件 test.py,手动计算锚定框:

import utils.autoanchor as autoAC
 
# 对数据集重新计算 anchors
new_anchors = autoAC.kmean_anchors('./data/mydata.yaml', 9, 640, 5.0, 1000, True)
print(new_anchors)

输入信息如下(只截取了部分):

autoanchor: Evolving anchors with Genetic Algorithm: fitness = 0.6604:  87%|████████▋ | 866/1000 [00:00<00:00, 2124.00it/s]autoanchor: thr=0.25: 0.9839 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.662-mean/best, past_thr=0.476-mean: 15,20,  38,25,  55,65,  131,87,  97,174,  139,291,  256,242,  368,382,  565,422
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  39,26,  54,64,  127,87,  97,176,  142,286,  257,245,  374,379,  582,424
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  39,26,  54,63,  126,86,  97,176,  143,285,  258,241,  369,381,  583,424
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  39,26,  54,63,  127,86,  97,176,  143,285,  258,241,  369,380,  583,424
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  39,26,  53,63,  127,86,  97,175,  143,284,  257,243,  369,381,  582,422
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  40,26,  53,62,  129,85,  96,175,  143,287,  256,240,  370,378,  582,419
autoanchor: Evolving anchors with Genetic Algorithm: fitness = 0.6605: 100%|██████████| 1000/1000 [00:00<00:00, 2170.29it/s]
Scanning '..\coco128\labels\train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100%|██████████| 128/128 [00:00<?, ?it/s]
autoanchor: thr=0.25: 0.9849 best possible recall, 3.84 anchors past thr
autoanchor: n=9, img_size=640, metric_all=0.267/0.663-mean/best, past_thr=0.476-mean: 15,20,  40,26,  53,62,  129,85,  96,175,  143,287,  256,240,  370,378,  582,419
[[     14.931      20.439]
 [     39.648       25.53]
 [     53.371       62.35]
 [     129.07      84.774]
 [     95.719      175.08]
 [     142.69      286.95]
 [     256.46      239.83]
 [      369.9       378.3]
 [     581.87      418.56]]
 
Process finished with exit code 0

输出的 9 组新的锚定框即是根据自己的数据集来计算的,可以按照顺序替换到你所使用的配置文件*.yaml中(比如 yolov5s.yaml)。就可以重新训练了。

参考的博文(表示感谢!):

https://github.com/ultralytics/yolov5

https://blog.csdn.net/flyfish1986/article/details/117594265

https://zhuanlan.zhihu.com/p/183838757

https://blog.csdn.net/aabbcccddd01/article/details/109578614

总结

到此这篇关于yolov5中anchors设置详解的文章就介绍到这了,更多相关yolov5 anchors设置内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!

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