Hello, I have a question about the nets.resnet_v1.resnet_v1_101() this function in the tensorflow. I observe the structure of this model and find that the output of last three layers are [1, 7, 7, 2048], [1, 7, 7, 1024], [1, 14, 14, 512]. However the output of last three layers of the normal resnet sholud be [1, 7, 7, 2048], [1, 14, 14, 1024], [1, 28, 28, 512].
So what I doubt is that if I use small size feature map [1, 7, 7, 1024] to build the class and box subnet, will it affect the model's result of objects of a certain size? Because the aim of different size of feature map is to detect objects in different scales
Hello, I have a question about the nets.resnet_v1.resnet_v1_101() this function in the tensorflow. I observe the structure of this model and find that the output of last three layers are [1, 7, 7, 2048], [1, 7, 7, 1024], [1, 14, 14, 512]. However the output of last three layers of the normal resnet sholud be [1, 7, 7, 2048], [1, 14, 14, 1024], [1, 28, 28, 512]. So what I doubt is that if I use small size feature map [1, 7, 7, 1024] to build the class and box subnet, will it affect the model's result of objects of a certain size? Because the aim of different size of feature map is to detect objects in different scales