myih

@Ghustwb In TensorRT4 the API allows you to specify the order/layout of the input, not in TensorRT3 though You can find description of the API [here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/structnvinfer1_1_1plugin_1_1_detection_output_parameters.html )

@Ghustwb Maybe try working with another repo like [this one](https://github.com/Teoge/tensorrt-ssd-easy) And check the plugin layer name carefully, when I worked on mobilenetSSD I was sure I had them correctly for...

@hammadj What do you mean by "merged the batch-norm layers into the conv layers", did you write a kernel that do BN after convolution? If so, are you willing to...

@NguyenHongHanh @saikumarGadde My mobilenet-ssd shape output is INPUT Tensor Shape is: C: 3 H: 300 W: 300 mbox_conf_softmax Tensor Shape is: C: 21 H: 1917 W: 1 mbox_loc Tensor Shape...

@JingliangGao So looks like my mbox_priorbox shape is incorrect? I'm using TensorRT4.0 with CUDA8, chuanqi305's weight, chenzhi1992's plugin prototxt and this @saikumarGadde's implementation with some modification. I use group convolution...

@JingliangGao Thank you. The output shapes are the same with Caffe's output. Here's a few things I not certain about, could you confirm that I can use group conv for...

@JingliangGao Thanks for the reply. Sorry I referenced the wrong code (from vgg-ssd), this is the one I'm trying with mobilenet-ssd TensorRT4: `createSSDDetectionOutputPlugin({true, false, 0, 21, 100, 100, 0.25, 0.45,...

@paghdv So that's why I got 6ms for mobilenet-ssd on GTX1080 (I couldn't test caffe's speed because there's some bugs when using cudnn)

@Ghustwb your output shape should be correct, when I was getting wrong result it's because the image loader is different in chuanqi305's repo, make sure you change that part