FRCNN from scratch

I wrote this fork to know FRCNN better, forking from frcnn-keras. Thought that RPN should be trained separately, so added that feature.

Compared to the forked keras-frcnn..

1. mobilenetv1 and mobilenetv2(TBD) support added (partially). Can also try Mobilenetv1_05,Mobilenetv1_25 for smaller nets.
2. VGG19 support added.
3. RPN can be trained seperately.

Note that you must download the imagenet pretrained model prior and place it in the root directory.

Look at https://github.com/keras-team/keras/tree/master/keras/applications for details.

e.g. to get VGG16 weights.. place it in pretrain directory.

wget https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5

Running scripts..

1. lets train region proposal network first, rather than training the whole network.

Training the entire faster-rcnn is quite difficult, but RPN itself can be more handy!

You can see if the loss converges.. etc

python train_rpn.py --network mobilenetv1 -o simple -p /path/to/your/dataset/

Epoch 1/20
100/100 [==============================] - 57s 574ms/step - loss: 5.2831 - rpn_out_class_loss: 4.8526 - rpn_out_regress_loss: 0.4305 - val_loss: 4.2840 - val_rpn_out_class_loss: 3.8344 - val_rpn_out_regress_loss: 0.4496
Epoch 2/20
100/100 [==============================] - 51s 511ms/step - loss: 4.1171 - rpn_out_class_loss: 3.7523 - rpn_out_regress_loss: 0.3649 - val_loss: 4.5257 - val_rpn_out_class_loss: 4.1379 - val_rpn_out_regress_loss: 0.3877
Epoch 3/20
100/100 [==============================] - 49s 493ms/step - loss: 3.4928 - rpn_out_class_loss: 3.1787 - rpn_out_regress_loss: 0.3142 - val_loss: 2.9241 - val_rpn_out_class_loss: 2.5502 - val_rpn_out_regress_loss: 0.3739
Epoch 4/20
 80/100 [=======================>......] - ETA: 9s - loss: 2.8467 - rpn_out_class_loss: 2.5729 - rpn_out_regress_loss: 0.2738  

2. then train the whole Faster-RCNN network!

python train_.frcnn.py --network mobilenetv1 -o simple -p /path/to/your/dataset/

Using TensorFlow backend.
Parsing annotation files
Training images per class:
{'Car': 1357, 'Cyclist': 182, 'Pedestrian': 5, 'bg': 0}
Num classes (including bg) = 4
Config has been written to config.pickle, and can be loaded when testing to ensure correct results
Num train samples 401
Num val samples 88
loading weights from ./pretrain/mobilenet_1_0_224_tf.h5
loading previous rpn model..
no previous model was loaded
Starting training
Epoch 1/200
100/100 [==============================] - 150s 2s/step - rpn_cls: 4.5333 - rpn_regr: 0.4783 - detector_cls: 1.2654 - detector_regr: 0.1691  
Mean number of bounding boxes from RPN overlapping ground truth boxes: 1.74
Classifier accuracy for bounding boxes from RPN: 0.935625
Loss RPN classifier: 4.244322432279587
Loss RPN regression: 0.4736669697239995
Loss Detector classifier: 1.1491613787412644
Loss Detector regression: 0.20629869312047958
Elapsed time: 150.15273475646973
Total loss decreased from inf to 6.07344947386533, saving weights
Epoch 2/200
Average number of overlapping bounding boxes from RPN = 1.74 for 100 previous iterations
 38/100 [==========>...................] - ETA: 1:24 - rpn_cls: 3.2813 - rpn_regr: 0.4576 - detector_cls: 0.8776 - detector_regr: 0.1826