Experimental result

[1292765944]

Cool code! I wonder whether you have reimplemented the exact results of the FPN paper?

Best! guangxing

[Yc174]

Have you trained this ?Can you tell me the results? @1292765944

[xmyqsh]

I'm starting training...

[chl916185]

When I was loading the trained model，I met this error： Traceback (most recent call last): File "./faster_rcnn/test_net.py", line 44, in network = get_network(network_name) File "./faster_rcnn/../lib/networks/factory.py", line 19, in get_network return FPN_test() File "./faster_rcnn/../lib/networks/FPN_test.py", line 26, in init self.setup() File "./faster_rcnn/../lib/networks/FPN_test.py", line 356, in setup .fc(n_classes, relu=False, name='cls_score')) File "./faster_rcnn/../lib/networks/network.py", line 34, in layer_decorated layer_output = op(self, layer_input, *args, **kwargs) File "./faster_rcnn/../lib/networks/network.py", line 407, in fc regularizer=self.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)) File "./faster_rcnn/../lib/networks/network.py", line 97, in make_var return tf.get_variable(name, shape, initializer=initializer, trainable=trainable, regularizer=regularizer) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 988, in get_variable custom_getter=custom_getter) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 890, in get_variable custom_getter=custom_getter) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 348, in get_variable validate_shape=validate_shape) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 333, in _true_getter caching_device=caching_device, validate_shape=validate_shape) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 639, in _get_single_variable name, "".join(traceback.format_list(tb)))) ValueError: Variable cls_score/weights already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

File "./faster_rcnn/../lib/networks/network.py", line 97, in make_var return tf.get_variable(name, shape, initializer=initializer, trainable=trainable, regularizer=regularizer) File "./faster_rcnn/../lib/networks/network.py", line 407, in fc regularizer=self.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)) File "./faster_rcnn/../lib/networks/network.py", line 34, in layer_decorated layer_output = op(self, layer_input, *args, **kwargs)

The reason is that： #========= RCNN ============ .fc(n_classes, relu=False, name='cls_score')) in lib/networks/FPN_test.py. @xmyqsh

[xmyqsh]

@1292765944 @Yc174 @chl916185

train result, rpn_loss is on the fly... What is the reason that may cause rpn_loss not converge, do you think ?

speed: 0.412s / iter image: 2008_000307.jpg iter: 199870 / 200000, total loss: 21.4851, rpn_loss_cls: 1.2544, rpn_loss_box: 19.9337, loss_cls: 0.2095, loss_box: 0.0876, lr: 0.0000 00 speed: 0.401s / iter image: 2008_005319.jpg iter: 199880 / 200000, total loss: 11.8511, rpn_loss_cls: 1.3227, rpn_loss_box: 10.2611, loss_cls: 0.2030, loss_box: 0.0643, lr: 0.0000 00 speed: 0.406s / iter image: 006483.jpg iter: 199890 / 200000, total loss: 25.3140, rpn_loss_cls: 1.1014, rpn_loss_box: 24.0750, loss_cls: 0.1176, loss_box: 0.0201, lr: 0.000000 speed: 0.395s / iter image: 002967.jpg iter: 199900 / 200000, total loss: 10.4668, rpn_loss_cls: 1.5063, rpn_loss_box: 8.6171, loss_cls: 0.2443, loss_box: 0.0990, lr: 0.000000 speed: 0.397s / iter image: 2011_001198.jpg iter: 199910 / 200000, total loss: 14.9206, rpn_loss_cls: 1.4058, rpn_loss_box: 13.2595, loss_cls: 0.2015, loss_box: 0.0537, lr: 0.000000 speed: 0.380s / iter image: 2008_003645.jpg iter: 199920 / 200000, total loss: 19.0740, rpn_loss_cls: 1.2057, rpn_loss_box: 17.1077, loss_cls: 0.5278, loss_box: 0.2328, lr: 0.000000 speed: 0.426s / iter image: 005811.jpg iter: 199930 / 200000, total loss: 10.8934, rpn_loss_cls: 1.4540, rpn_loss_box: 9.0603, loss_cls: 0.2846, loss_box: 0.0945, lr: 0.000000 speed: 0.439s / iter image: 2009_003860.jpg iter: 199940 / 200000, total loss: 14.3258, rpn_loss_cls: 1.6082, rpn_loss_box: 12.5498, loss_cls: 0.1635, loss_box: 0.0044, lr: 0.000000 speed: 0.388s / iter image: 2011_000556.jpg iter: 199950 / 200000, total loss: 22.7038, rpn_loss_cls: 1.2282, rpn_loss_box: 20.8446, loss_cls: 0.3576, loss_box: 0.2734, lr: 0.000000 speed: 0.397s / iter image: 2008_004585.jpg iter: 199960 / 200000, total loss: 11.1179, rpn_loss_cls: 1.3557, rpn_loss_box: 9.2402, loss_cls: 0.3512, loss_box: 0.1708, lr: 0.000000 speed: 0.346s / iter image: 2008_000393.jpg iter: 199970 / 200000, total loss: 11.2609, rpn_loss_cls: 1.7995, rpn_loss_box: 8.7590, loss_cls: 0.4851, loss_box: 0.2173, lr: 0.000000 speed: 0.371s / iter image: 2011_003081.jpg iter: 199980 / 200000, total loss: 10.4372, rpn_loss_cls: 1.4303, rpn_loss_box: 8.8616, loss_cls: 0.1440, loss_box: 0.0013, lr: 0.000000 speed: 0.342s / iter image: 2008_007069.jpg iter: 199990 / 200000, total loss: 19.8492, rpn_loss_cls: 1.3173, rpn_loss_box: 18.3676, loss_cls: 0.1637, loss_box: 0.0005, lr: 0.000000 speed: 0.356s / iter

[chl916185]

the lr is 0.000000,why？ @xmyqsh

[1292765944]

@xmyqsh I met similar problem. It is not easy to converge using the params in FPN paper. such as 800 pixels of the shorter side, including anchor boxes that are outside the image.