(三) COCO Python API - 数据集类数量分布

COCO 数据集中各类数量的分布到底是怎样的?

如果一个数据集中各类数量分布差异很大, 是否会对深度学习模型训练有影响? 为什么?

如果有影响, 那又应该如何处理?

初步分析

以下这段代码给出了 COCO 数据集 val2017 中 80 类的图片数据和标注数据的数量.

from pycocotools.coco import COCO

dataDir='/path/to/your/cocoDataset'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)

# initialize COCO api for instance annotations
coco=COCO(annFile)

# display COCO categories and supercategories
cats = coco.loadCats(coco.getCatIds())
cat_nms=[cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(cat_nms)))

# 统计各类的图片数量和标注框数量
for cat_name in cat_nms:
    catId = coco.getCatIds(catNms=cat_name)
    imgId = coco.getImgIds(catIds=catId)
    annId = coco.getAnnIds(imgIds=imgId, catIds=catId, iscrowd=None)

    print("{:<15} {:<6d}     {:<10d}".format(cat_name, len(imgId), len(annId)))

输出信息如下:

category	图片数量	标注框数量	category	图片数量	标注框数量
person	2693	11004	bicycle	149	316
car	535	1932	motorcycle	159	371
airplane	97	143	bus	189	285
train	157	190	truck	250	415
boat	121	430	traffic light	191	637
fire hydrant	86	101	stop sign	69	75
parking meter	37	60	bench	235	413
bird	125	440	cat	184	202
dog	177	218	horse	128	273
sheep	65	361	cow	87	380
elephant	89	255	bear	49	71
zebra	85	268	giraffe	101	232
backpack	228	371	umbrella	174	413
handbag	292	540	tie	145	254
suitcase	105	303	frisbee	84	115
skis	120	241	snowboard	49	69
sports ball	169	263	kite	91	336
baseball bat	97	146	baseball glove	100	148
skateboard	127	179	surfboard	149	269
tennis racket	167	225	bottle	379	1025
wine glass	110	343	cup	390	899
fork	155	215	knife	181	326
spoon	153	253	bowl	314	626
banana	103	379	apple	76	239
sandwich	98	177	orange	85	287
broccoli	71	316	carrot	3	17
hot dog	0	345	pizza	153	285
donut	62	338	cake	124	316
chair	580	1791	couch	195	261
potted plant	172	343	bed	149	163
dining table	501	697	toilet	149	179
tv	207	288	laptop	183	231
mouse	88	106	remote	145	283
keyboard	106	153	cell phone	214	262
microwave	54	55	oven	115	143
toaster	8	9	sink	187	225
refrigerator	101	126	book	230	1161
clock	204	267	vase	137	277
scissors	28	36	teddy bear	0	262
hair drier	9	11	toothbrush	34	57

可以看出, 不管是各类的图片数目还是标注框数目, 其数量分布差异均很大. 特别是 ‘person’ 类的标注框数目明显要其中大多数类的标注框数目大约多出 50 倍.

2017train 类数目分布

以下是 COCO 数据集 train2017 中 80 类的图片数据和标注数据的数量.

category	图片数量	标注框数量	category	图片数量	标注框数量
person	64115	262465	bicycle	3252	7113
car	12251	43867	motorcycle	3502	8725
airplane	2986	5135	bus	3952	6069
train	3588	4571	truck	6127	9973
boat	3025	10759	traffic light	4139	12884
fire hydrant	1711	1865	stop sign	1734	1983
parking meter	705	1285	bench	5570	9838
bird	3237	10806	cat	4114	4768
dog	4385	5508	horse	2941	6587
sheep	1529	9509	cow	1968	8147
elephant	2143	5513	bear	960	1294
zebra	1916	5303	giraffe	2546	5131
backpack	5528	8720	umbrella	3968	11431
handbag	6841	12354	tie	3810	6496
suitcase	2402	6192	frisbee	2184	2682
skis	3082	6646	snowboard	1654	2685
sports ball	4262	6347	kite	2261	9076
baseball bat	2506	3276	baseball glove	2629	3747
skateboard	3476	5543	surfboard	3486	6126
tennis racket	3394	4812	bottle	8501	24342
wine glass	2533	7913	cup	9189	20650
fork	3555	5479	knife	4326	7770
spoon	3529	6165	bowl	7111	14358
banana	2243	9458	apple	1586	5851
sandwich	2365	4373	orange	1699	6399
broccoli	1939	7308	carrot	24	142
hot dog	11	29	pizza	3166	5821
donut	1523	7179	cake	2925	6353
chair	12774	38491	couch	4423	5779
potted plant	4452	8652	bed	3682	4192
dining table	11837	15714	toilet	3353	4157
tv	4561	5805	laptop	3524	4970
mouse	1876	2262	remote	3076	5703
keyboard	2115	2855	cell phone	4803	6434
microwave	1547	1673	oven	2877	3334
toaster	217	225	sink	4678	5610
refrigerator	2360	2637	book	5332	24715
clock	4659	6334	vase	3593	6613
scissors	947	1481	teddy bear	16	92
hair drier	189	198	toothbrush	1007	1954

(三) COCO Python API - 数据集类数量分布

初步分析

2017train 类数目分布

TODO…

猜你喜欢