diff --git a/mmpose/configs/new_config.py b/mmpose/configs/new_config.py
new file mode 100644
index 0000000000..64d340f96e
--- /dev/null
+++ b/mmpose/configs/new_config.py
@@ -0,0 +1,189 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from mmpose.configs._base_.default_runtime import *
+
+from mmengine.dataset import DefaultSampler
+from mmengine.model import PretrainedInit
+from mmengine.optim import LinearLR, MultiStepLR
+from torch.optim import Adam
+
+from mmpose.codecs import MSRAHeatmap
+from mmpose.datasets import OneHand10KDataset
+from mmpose.datasets import GenerateTarget
+from mmpose.datasets import GetBBoxCenterScale
+from mmpose.datasets import LoadImage
+from mmpose.datasets import PackPoseInputs
+from mmpose.datasets import RandomFlip
+from mmpose.datasets import TopdownAffine
+from mmpose.datasets.transforms.common_transforms import RandomBBoxTransform
+from mmpose.evaluation import PCKAccuracy, AUC, EPE
+from mmpose.models import HeatmapHead
+from mmpose.models import HRNet
+from mmpose.models import KeypointMSELoss
+from mmpose.models import FeatureMapProcessor
+from mmpose.models import PoseDataPreprocessor
+from mmpose.models import TopdownPoseEstimator
+#
+
+# runtime
+train_cfg.update(max_epochs=210, val_interval=10)
+
+# optimizer
+optim_wrapper = dict(optimizer=dict(
+    type=Adam,
+    lr=5e-4,
+))
+
+# learning policy
+param_scheduler = [
+    dict(type=LinearLR, begin=0, end=500, start_factor=0.001,
+         by_epoch=False),  # warm-up
+    dict(
+        type=MultiStepLR,
+        begin=0,
+        end=210,
+        milestones=[170, 200],
+        gamma=0.1,
+        by_epoch=True)
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# hooks
+default_hooks.update(checkpoint=dict(save_best='AUC', rule='greater'))
+
+# codec settings
+codec = dict(
+    type=MSRAHeatmap,
+    input_size=(256, 256),
+    heatmap_size=(64, 64),
+    sigma=2,
+    unbiased=True
+)
+
+# model settings
+model = dict(
+    type=TopdownPoseEstimator,
+    data_preprocessor=dict(
+        type=PoseDataPreprocessor,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type=HRNet,
+        in_channels=3,
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(18, 36)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(18, 36, 72)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(18, 36, 72, 144),
+                multiscale_output=True),
+            upsample=dict(mode='bilinear', align_corners=False)),
+        init_cfg=dict(
+            type=PretrainedInit,
+            checkpoint='open-mmlab://msra/hrnetv2_w18',
+        )),
+    neck=dict(
+        type=FeatureMapProcessor,
+        concat=True,
+    ),
+    head=dict(
+        type=HeatmapHead,
+        in_channels=270,
+        out_channels=21,
+        deconv_out_channels=None,
+        conv_out_channels=(270, ),
+        conv_kernel_sizes=(1, ),
+        loss=dict(type=KeypointMSELoss, use_target_weight=True),
+        decoder=codec),
+    test_cfg=dict(
+        flip_test=True,
+        flip_mode='heatmap',
+        shift_heatmap=True,
+    ))
+
+# base dataset settings
+dataset_type = OneHand10KDataset
+data_mode = 'topdown'
+data_root = 'data/pose/OneHand10K/'
+
+# pipelines
+train_pipeline = [
+    dict(type=LoadImage),
+    dict(type=GetBBoxCenterScale),
+    dict(type=RandomFlip, direction='horizontal'),
+    dict(
+        type=RandomBBoxTransform, rotate_factor=180,
+        scale_factor=(0.7, 1.3)),
+    dict(type=TopdownAffine, input_size=codec['input_size']),
+    dict(type=GenerateTarget, encoder=codec),
+    dict(type=PackPoseInputs)
+]
+val_pipeline = [
+    dict(type=LoadImage),
+    dict(type=GetBBoxCenterScale),
+    dict(type=TopdownAffine, input_size=codec['input_size']),
+    dict(type=PackPoseInputs)
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/onehand10k_train.json',
+        data_prefix=dict(img=''),
+        pipeline=train_pipeline,
+    ))
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/onehand10k_test.json',
+        data_prefix=dict(img=''),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# evaluators
+val_evaluator = [
+    dict(type=PCKAccuracy, thr=0.2),
+    dict(type=AUC),
+    dict(type=EPE),
+]
+test_evaluator = val_evaluator