Any tip to train models from scratch using cityscape dataset? #253
Description
Hi, I'm trying to train the segmentation models using cityscape dataset(training: 2975, validation:500).
In the papers of some segmentation models, mIoU of them is over 70%.
However, I cannot reach there.
I tried pspnet, segnet, and fcns, but they just reached to 40% of mIoU.
I also tried data augmentation(scaling, random rotation, horizontal flip).
In order to ignore the background, I changed the ignore_index of loss function from 250 to 0, and the label of 250 is replaced to 0.
Did I do something wrong or is there any tip to train them from scratch?
Thanks for your help in advance.
My cfg file of pspnet is below.
model:
arch: pspnet
data:
dataset: cityscapes
train_split: train
val_split: val
img_rows: 512
img_cols: 1024
path: '/home/irobot/hj/Dataset/cityscape/'
training:
train_iters: 120000
batch_size: 16
val_interval: 1000
n_workers: 16
print_interval: 20
optimizer:
name: 'sgd'
lr: 1.0e-3
weight_decay: 0.0001
momentum: 0.9
loss:
name: 'multi_scale_cross_entropy'
size_average: True
lr_schedule:
name: 'multi_step'
milestones: [30000, 60000, 90000]
resume: pspnet_cityscape_best_model.pkl
augmentations:
rotate: 10 #[rotate -d to d degrees]
scale: 2048 #[scale to size (h,w)]
hflip: 0.5 #[flip horizontally with chance p]