-
Simple faster-RCNN codes in Keras!
-
RPN (region proposal layer) can be trained separately!
-
Active support! :)
-
MobileNetv1 & v2 support!
-
VGG support!
-
added eval for pascal_voc :)
Stars and forks are appreciated if this repo helps your project, will motivate me to support this repo.
PR and issues will help too!
Thanks :)
Tested with Tensorflow==1.12.0 and Keras 2.2.4.
- Global Wheat Detection
Nice kernel by kishor1210
- mobilenetv1 and mobilenetv2 supported. Can also try Mobilenetv1_05,Mobilenetv1_25 for smaller nets on the Edge.
- VGG19 support added.
- RPN can be trained seperately.
https://github.com/kentaroy47/ObjectDetection.Pytorch
Here are my object detection models in Pytorch. The model is SSD and trains quite fast.
vgg16
https://drive.google.com/file/d/1IgxPP0aI5pxyPHVSM2ZJjN1p9dtE4_64/view?usp=sharing
config.pickle:
https://drive.google.com/open?id=1BL_2ZgTf55vH2q1jvVz0hkhlWYgj-coa
git clone https://github.com/kentaroy47/frcnn-from-scratch-with-keras.git
cd frcnn-from-scratch-with-keras
Install requirements. make sure that you have Keras installed.
pip install -r requirements.txt
Using imagenet pretrained VGG16 weights will significantly speed up training.
Download and place it in the root directory.
You can choose other base models as well.
# place weights in pretrain dir.
mkdir pretrain & mv pretrain
# download models you would like to use.
# for VGG16
wget https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5
# for mobilenetv1
wget https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5
# for mobilenetv2
wget https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5
# for resnet 50
wget https://github.com/fchollet/deep-learning-models/releases/download/v0.1/resnet50_weights_tf_dim_ordering_tf_kernels.h5
Other tensorflow pretrained models are in bellow.
https://github.com/fchollet/deep-learning-models/releases/
Training the entire faster-rcnn is quite difficult, but RPN itself can be more handy!
You can see if the loss converges.. etc
Other network options are: resnet50, mobilenetv1, vgg19.
python train_rpn.py --network vgg -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
I recommend using the pretrained RPN model, which will stablize training. You can download the rpn model (VGG16) from here: https://drive.google.com/file/d/1IgxPP0aI5pxyPHVSM2ZJjN1p9dtE4_64/view?usp=sharing
# sample command
python train_frcnn.py --network vgg -o simple -p /path/to/your/dataset/
# using the rpn trained in step.3 will make the training more stable.
python train_frcnn.py --network vgg -o simple -p /path/to/your/dataset/ --rpn models/rpn/rpn.vgg.weights.36-1.42.hdf5
# sample command to train PASCAL_VOC dataset:
python train_frcnn.py -p ../VOCdevkit/ --lr 1e-4 --opt SGD --network vgg --elen 1000 --num_epoch 100 --hf
# this may take about 12 hours with GPU..
# add --load yourmodelpath if you want to resume training.
python train_frcnn.py --network vgg16 -o simple -p /path/to/your/dataset/ --load model_frcnn.hdf5
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
For evaluation and getting mAP, please take a look at eval.ipynb
See issue #6 and look for help.
You can either try voc or simple parsers for your dataset.
simple parsers are much easier, while you train your network as:
python train_rpn.py --network vgg16 -o simple -p ./dataset.txt
Simply provide a text file, with each line containing:
filepath,x1,y1,x2,y2,class_name
For example:
/data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_002.jpg,215,312,279,391,catYou can do labeling with tools. I highly recommend Labelme, which is easy to use.
https://github.com/wkentaro/labelme
you can directly output VOC-like dataset from your labeled results.
look at the example below.
https://github.com/kentaroy47/labelme-voc-format/tree/master/examples
There are other tools like Labellmg too, if interested.
https://github.com/tzutalin/labelImg
download dataset and extract.
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar -xf VOCtrainval_06-Nov-2007.tar
tar -xf VOCtest_06-Nov-2007.tar
tar -xf VOCtrainval_11-May-2012.tar
then run training
python train_frcnn.py --network mobilenetv1 -p ./VOCdevkit
Using TensorFlow backend.
data path: ['VOCdevkit/VOC2007']
Parsing annotation files
[Errno 2] No such file or directory: 'VOCdevkit/VOC2007/ImageSets/Main/test.txt'
Training images per class:
{'aeroplane': 331,
'bg': 0,
'bicycle': 418,
'bird': 599,
'boat': 398,
'bottle': 634,
'bus': 272,
'car': 1644,
'cat': 389,
'chair': 1432,
'cow': 356,
'diningtable': 310,
'dog': 538,
'horse': 406,
'motorbike': 390,
'person': 5447,
'pottedplant': 625,
'sheep': 353,
'sofa': 425,
'train': 328,
'tvmonitor': 367}
Num classes (including bg) = 21
Config has been written to config.pickle, and can be loaded when testing to ensure correct results
Num train samples 5011
Num val samples 0
Instructions for updating:
Colocations handled automatically by placer.
loading weights from ./pretrain/mobilenet_1_0_224_tf.h5
loading previous rpn model..
no previous model was loaded
Starting training
Epoch 1/200
Instructions for updating:
Use tf.cast instead.
23/1000 [..............................] - ETA: 43:30 - rpn_cls: 7.3691 - rpn_regr: 0.1865 - detector_cls: 3.0206 - detector_regr: 0.3050