提交 16984f5f 编写于 作者: W wqz960

add ResNeSt

上级 feac3ca4
mode: 'train'
ARCHITECTURE:
name: 'ResNeSt50'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1281167
save_interval: 1
validate: True
valid_interval: 1
epochs: 300
topk: 5
image_shape: [3, 224, 224]
use_mix: True
ls_epsilon: 0.1
LEARNING_RATE:
function: 'CosineWarmup'
params:
lr: 0.1
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.000070
TRAIN:
batch_size: 256
num_workers: 4
file_list: "./dataset/ILSVRC2012/train_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- AutoAugment:
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
mix:
- CutmixOperator:
alpha: 0.2
VALID:
batch_size: 64
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
# ResNeSt以及RegNet网络
## 概述
ResNeSt系列模型是在2020年提出的,在原有的resnet网络结构上做了改进,通过引入K个Group和在不同Group中加入类似于SEBlock的attention模块,使得精度相比于基础模型ResNet有了大幅度的提高,且参数量和flops与基础的ResNet基本保持一致。
## 精度、FLOPS和参数量
| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ResNeSt50 | 0.8102 | 0.9542| 0.8113 | -|5.39 | 27.5 |
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