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All Projects → qjadud1994 → RetinaNet_tensorflow

qjadud1994 / RetinaNet_tensorflow

Licence: MIT license
For easier and more readable tensorflow codes

Programming Languages

python
139335 projects - #7 most used programming language

RetinaNet_tensorflow

For easier and more readable tensorflow codes

How to use

  • For Trainig (recommend to use the default parameters)
python tfrecord/tfrecord_VOC.py
CUDA_VISIBLE_DEVICES=0,1 python train.py
  • For Testing (recommend to use the default parameters)
CUDA_VISIBLE_DEVICES=0 python test.py

Results

screensh

Todo list:

  • multi-gpu code
  • Training visualize using Tensorboard
  • validation output image visualization using Tensorboard
  • Choose BatchNorm model or GroupNorm model
  • Choose Trainable BatchNorm(not working!) or Freeze BatchNorm
  • (BatchNorm mode) Get Imagenet pre-trained weights from resnet50.pth
  • (GroupNorm mode) Get Imagenet pre-trained weights from resnet50_groupnorm32.tar
  • tf.train.batch -> tf.train.shuffle_batch
  • add augmentation ( + random crop)
  • use SE-resnet backbone
  • add evaluation (mAP) code
  • change upsample function for 600x600 input
  • Training/Validation Error ( % value)

Description

File Description
train.py Train RetinaNet
test.py Inference RetinaNet
tfrecord/tfrecord_VOC. py Make VOC tfrecord
Detector/layers. py layer functions used in RetinaNet
Detector/RetinaNet. py Define RetinaNet

Environment

  • os : Ubuntu 16.04.4 LTS
  • GPU : Tesla P40 (24GB)
  • Python : 3.6.6
  • Tensorflow : 1.10.0
  • CUDA, CUDNN : 9.0, 7.1.3
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