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
```

screensh

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

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