yolov5 anchors设置详解
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):
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
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
来源:高祥xiang