Check Out the weights path from Keras_frcnn.Config file last line and parser paths for each of the train_rpn.py, train_frcnn.py and test_frcnn.py
We have already provided default paths and other values so please check it out ones before executing. Code is 100% working
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Simple faster-RCNN codes in Keras!
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RPN (region proposal layer) can be trained separately!
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Active support! :)
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MobileNet support!
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VGG support!
- mobilenetv1 and mobilenetv2(TBD) support added (partially). Can also try Mobilenetv1_05,Mobilenetv1_25 for smaller nets.
- VGG19 support added.
- RPN can be trained seperately.
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.
# 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 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
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
python train_.frcnn.py --network mobilenetv1 -o simple -p /path/to/your/dataset/
Using TensorFlow backend.
Parsing annotation files
Training images per class:
{*, 'bg': 0}
Num classes (including bg) = 2
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
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,* /data/imgs/img_002.jpg,215,312,279,391,*
Note : Here * means class name which includes both Pedestrain and Cyclist.
Dataset is available here
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