参考文章：c(15条消息) yolov5 anchors设置详解_高祥xiang的博客-CSDN博客https://blog.csdn.net/qq_27278957/article/details/120036450一、yolov7的默认锚框都在 .\cfg\training 的yaml文件里面，比如yolov7.yaml

# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32

其中， 每一行代表应用不同的特征图；一行最大，二行中等，三行最小

二、自定义锚框

yolov5和yolov7带有自适应锚框

具体代码在  ..\utils\autoanchor.py 文件里面，

代码如下：

1.训练时自动计算锚框：

def check_anchors(dataset, model, thr=4.0, imgsz=640):
    # Check anchor fit to data, recompute if necessary
    prefix = colorstr('autoanchor: ')
    print(f'\n{prefix}Analyzing anchors... ', end='')
    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh

    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

    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
    bpr, aat = metric(anchors)
    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
    if bpr < 0.98:  # threshold to recompute
        print('. Attempting to improve anchors, please wait...')
        na = m.anchor_grid.numel() // 2  # number of anchors
        try:
            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
        except Exception as e:
            print(f'{prefix}ERROR: {e}')
        new_bpr = metric(anchors)[0]
        if new_bpr > bpr:  # replace anchors
            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
            check_anchor_order(m)
            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
        else:
            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
    print('')  # newline

 其中的主要参数是 bpr和aat

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）。

2.重新计算符合数据集的锚框代码如下：

def kmean_anchors(path='./data/coco.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
    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
    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）：

path：包含数据集文件路径等相关信息的 yaml 文件（比如 coco128.yaml）， 或者 数据集张量（yolov5 自动计算锚定框时就是用的这种方式，先把数据集标签信息读取再处理）
n：锚定框的数量，即有几组；默认值是9
img_size：图像尺寸。计算数据集样本标签框的宽高比时，是需要缩放到 img_size 大小后再计算的；默认值是640
thr：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；默认值是4.0；自动计算时，会自动根据你所使用的数据集，来计算合适的阈值。
gen：kmean聚类算法迭代次数，默认值是1000
verbose：是否打印输出所有计算结果，默认值是true
————————————————
版权声明：本文为CSDN博主「高祥xiang」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_27278957/article/details/120036450

不自动计算锚框可以在训练时设置 default=False

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

 三、最重要的来了，如果bpr小于0.98，没有自动计算锚框，可以——手动计算锚框

新建一个py文件，手动计算锚框：

import utils.autoanchor as autoAC
 
# 对数据集重新计算 anchors
new_anchors = autoAC.kmean_anchors('你自己数据集的配置文件', 9, 640, 5.0, 1000, True)
print(new_anchors)

其中，9代表聚类出9种锚框，640代表默认的图片大小，5.0表示数据集中标注框宽高比的最大阈值，1000代表kmean聚类算法迭代计算1000次。运行即可。

我的结果如下（数据集是公开数据集）：

[[     7.2368      6.6779]
 [      13.46       11.71]
 [     8.3749      21.515]
 [     24.094      20.985]
 [     56.135      21.595]
 [     38.372      38.616]
 [     89.473       37.31]
 [     79.463      74.843]
 [     160.38      110.27]]

然后我把9种结果四舍五入修改进了yaml配置文件中，

# anchors
anchors:
  - [7.5,7, 13.5,12, 8.5,22]  # P3/8
  - [24.1,21, 56.2,22, 38.5,39]  # P4/16
  - [89.5,37.5, 79.5,75, 160.5,110.5]  # P5/32

接下来，重新运行，使用yolov7.pt预训练，网络架构使用yolov7-tiny.yaml，运行100轮，

结果如下：

  Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████| 69/69 [00:27<00:00,  2.50it/s]
                 all        2189       73533        0.87       0.725       0.816       0.521
                 car        2189       42020       0.895       0.845       0.913       0.645
               truck        2189        2204       0.917       0.793        0.84       0.555
                 van        2189        3346       0.836       0.642       0.742       0.555
                 bus        2189        2540       0.897       0.685       0.786       0.541
          pedestrian        2189        7208       0.823       0.676       0.764       0.362
             cyclist        2189        5484       0.831       0.738        0.83       0.496
          tricyclist        2189        2152       0.906       0.712       0.831       0.513
        motorcyclist        2189        8579       0.854       0.713       0.826       0.501
100 epochs completed in 4.091 hours.

运行test.py文件，结果如下

                Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████| 69/69 [00:29<00:00,  2.36it/s]
                 all        2189       73533       0.866        0.72       0.814        0.52
                 car        2189       42020       0.888       0.841       0.912       0.644
               truck        2189        2204       0.905        0.79       0.839       0.554
                 van        2189        3346       0.842       0.634       0.738       0.553
                 bus        2189        2540       0.894       0.675       0.783        0.54
          pedestrian        2189        7208       0.814        0.67       0.761       0.362
             cyclist        2189        5484        0.83       0.732       0.827       0.494
          tricyclist        2189        2152       0.906       0.707       0.831       0.513
        motorcyclist        2189        8579       0.848       0.709       0.823       0.499
Speed: 0.6/0.8/1.5 ms inference/NMS/total per 640x640 image at batch-size 32

fps=1000/（0.6+0.8+1.5）=344.827

相比之前的结果如下

                  Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████| 69/69 [00:29<00:00,  2.36it/s]
                 all        2189       73533        0.85       0.707       0.792       0.509
                 car        2189       42020       0.868       0.834       0.888       0.633
               truck        2189        2204       0.901       0.764       0.824       0.556
                 van        2189        3346       0.793       0.621       0.716       0.542
                 bus        2189        2540       0.927       0.642       0.763       0.537
          pedestrian        2189        7208       0.794       0.662       0.741       0.353
             cyclist        2189        5484       0.794       0.742       0.815       0.487
          tricyclist        2189        2152       0.885       0.694       0.779       0.475
        motorcyclist        2189        8579       0.834       0.701       0.807       0.487
Speed: 0.6/0.8/1.5 ms inference/NMS/total per 640x640 image at batch-size 32
Fps=1000/（0.6+0.8+1.5）=344.827

对比两次结果，修改了锚框以后，精确值提升了1.6%，召回率提升了1.3%，mAP0.5提升了2.2%。效果还是有的。。。。。。