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Add CIFAR10 configs and models (#38) 

* rename cifar configs

* add cifar10 configs

* fix linting

* add params, flops and accuracy in docs

* add cifar10 pretrained models and logs

* use oss-accelerate url

* del unused cifar10 configs
pull/40/head
Lei Yang 2020-08-26 16:34:15 +08:00 committed by GitHub
parent b2fc837e84
commit 4b46fd6dc7
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13 changed files with 86 additions and 13 deletions
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2
configs/_base_/datasets/cifar10.py → configs/_base_/datasets/cifar10_bs16.py
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@ -19,7 +19,7 @@ test_pipeline = [
dict(type='Collect', keys=['img', 'gt_label'])
]
data = dict(
samples_per_gpu=128,
samples_per_gpu=16,
workers_per_gpu=2,
train=dict(
type=dataset_type, data_prefix='data/cifar10',

16
configs/_base_/models/resnet101_cifar.py 100644
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@ -0,0 +1,16 @@
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))

16
configs/_base_/models/resnet152_cifar.py 100644
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@ -0,0 +1,16 @@
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))

16
configs/_base_/models/resnet34_cifar.py 100644
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@ -0,0 +1,16 @@
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))

0
configs/_base_/schedules/cifar10.py → configs/_base_/schedules/cifar10_bs128.py
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5
configs/cifar10/resnet101_b16x8.py 100644
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@ -0,0 +1,5 @@
_base_ = [
'../_base_/models/resnet101_cifar.py',
'../_base_/datasets/cifar10_bs16.py',
'../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
]

5
configs/cifar10/resnet152_b16x8.py 100644
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@ -0,0 +1,5 @@
_base_ = [
'../_base_/models/resnet152_cifar.py',
'../_base_/datasets/cifar10_bs16.py',
'../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
]

4
configs/cifar10/resnet18.py
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@ -1,4 +0,0 @@
_base_ = [
'../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10.py',
'../_base_/schedules/cifar10.py', '../_base_/default_runtime.py'
]

4
configs/cifar10/resnet18_b16x8.py 100644
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@ -0,0 +1,4 @@
_base_ = [
'../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10_bs16.py',
'../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
]

4
configs/cifar10/resnet34_b16x8.py 100644
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@ -0,0 +1,4 @@
_base_ = [
'../_base_/models/resnet34_cifar.py', '../_base_/datasets/cifar10_bs16.py',
'../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
]

4
configs/cifar10/resnet50.py
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@ -1,4 +0,0 @@
_base_ = [
'../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10.py',
'../_base_/schedules/cifar10.py', '../_base_/default_runtime.py'
]

4
configs/cifar10/resnet50_b16x8.py 100644
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@ -0,0 +1,4 @@
_base_ = [
'../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py',
'../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
]

19
docs/model_zoo.md
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@ -20,10 +20,21 @@ The ResNet family models below are trained by standard data augmentations, i.e.,
| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.7 | 94.34 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext101_32x4d_batch256_20200708-87f2d1c9.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext101_32x4d_batch256_20200708-87f2d1c9.log.json) |
| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.22 | 94.52 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext101_32x8d_batch256_20200708-1ec34aa7.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext101_32x8d_batch256_20200708-1ec34aa7.log.json) |
| ResNeXt-32x4d-152 | 59.95 | 11.8 | 79.06 | 94.47 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext152_32x4d_batch256_20200708-aab5034c.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/resnext152_32x4d_batch256_20200708-aab5034c.log.json) |
| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200804-ae206104.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200708-657b3c36.log.json) |
| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200804-ba5b51d4.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200708-038a4d04.log.json) |
| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.log.json) |
| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200812-5bf4721e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200804-8860eec9.log.json) |
| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200804-ae206104.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200708-657b3c36.log.json) |
| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200804-ba5b51d4.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200708-038a4d04.log.json) |
| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.log.json) |
| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200812-5bf4721e.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200804-8860eec9.log.json) |
| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/mobilenet_v2_batch256_20200708-3b2dc3af.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/imagenet/mobilenet_v2_batch256_20200708-3b2dc3af.log.json) |
Models with * are converted from other repos, others are trained by ourselves.
## CIFAR10
| Model | Params(M) | Flops(G) | Top-1 (%) | Download |
|:---------------------:|:---------:|:--------:|:---------:|:--------:|
| ResNet-18-b16x8 | 11.17 | 0.56 | 94.72 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet18_b16x8_20200823-f906fa4e.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet18_b16x8_20200823-f906fa4e.log.json) |
| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet34_b16x8_20200823-52d5d832.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet34_b16x8_20200823-52d5d832.log.json) |
| ResNet-50-b16x8 | 23.52 | 1.31 | 95.36 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet50_b16x8_20200823-882aa7b1.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet50_b16x8_20200823-882aa7b1.log.json) |
| ResNet-101-b16x8 | 42.51 | 2.52 | 95.66 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet101_b16x8_20200823-d9501bbc.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet101_b16x8_20200823-d9501bbc.log.json) |
| ResNet-152-b16x8 | 58.16 | 3.74 | 95.96 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet152_b16x8_20200823-ad4d5d0c.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmclassification/v0/cifar10/resnet152_b16x8_20200823-ad4d5d0c.log.json) |