The simplest way to use the custom dataset is to convert your annotation format to COCO dataset format.
The annotation json files in COCO format has the following necessary keys:
'images': [
{
'file_name': '000000001268.jpg',
'height': 427,
'width': 640,
'id': 1268
},
...
],
'annotations': [
{
'segmentation': [[426.36,
...
424.34,
223.3]],
'keypoints': [0,0,0,
0,0,0,
0,0,0,
427,220,2,
443,222,2,
414,228,2,
449,232,2,
408,248,1,
454,261,2,
0,0,0,
0,0,0,
411,287,2,
431,287,2,
0,0,0,
458,265,2,
0,0,0,
466,300,1],
'num_keypoints': 10,
'area': 3894.5826,
'iscrowd': 0,
'image_id': 1268,
'bbox': [402.34, 205.02, 65.26, 88.45],
'category_id': 1,
'id': 215218
},
...
],
'categories': [
{'id': 1, 'name': 'person'},
]There are three necessary keys in the json file:
images: contains a list of images with their information likefile_name,height,width, andid.annotations: contains the list of instance annotations.categories: contains the category name ('person') and its ID (1).
Add a new dataset info config file.
configs/_base_/datasets/custom.py
An example of the dataset config is as follows.
keypoint_info contains the information about each keypoint.
name: the keypoint name. The keypoint name must be unique.id: the keypoint id.color: ([B, G, R]) is used for keypoint visualization.type: 'upper' or 'lower', will be used in data augmetation.swap: indicates the 'swap pair' (also known as 'flip pair'). When applying image horizontal flip, the left part will become the right part. We need to flip the keypoints accordingly.
skeleton_info contains the information about the keypoint connectivity, which is used for visualization.
joint_weights assigns different loss weights to different keypoints.
sigmas is used to calculate the OKS score. Please read keypoints-eval to learn more about it.
dataset_info = dict(
dataset_name='coco',
paper_info=dict(
author='Lin, Tsung-Yi and Maire, Michael and '
'Belongie, Serge and Hays, James and '
'Perona, Pietro and Ramanan, Deva and '
r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
title='Microsoft coco: Common objects in context',
container='European conference on computer vision',
year='2014',
homepage='http://cocodataset.org/',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
-
First create a package inside the mmpose/datasets/datasets folder.
-
Create a class definition of your dataset in the package folder and register it in the registry with a name. Without a name, it will keep giving the error.
KeyError: 'XXXXX is not in the dataset registry'@DATASETS.register_module(name='MyCustomDataset') class MyCustomDataset(SomeOtherBaseClassAsPerYourNeed): -
Make sure you have updated the
__init__.pyof your package folder -
Make sure you have updated the
__init__.pyof the dataset package folder.
Create a custom training config file as per your need and the model/architecture you want to use in the configs folder. You may modify an existing config file to use the new custom dataset.
In configs/my_custom_config.py:
...
# dataset settings
dataset_type = 'MyCustomDataset'
...
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file='path/to/your/train/json',
img_prefix='path/to/your/train/img',
...),
val=dict(
type=dataset_type,
ann_file='path/to/your/val/json',
img_prefix='path/to/your/val/img',
...),
test=dict(
type=dataset_type,
ann_file='path/to/your/test/json',
img_prefix='path/to/your/test/img',
...))
...Make sure you have provided all the paths correctly.