First, thank you for your great research and contribution.

I really need tensorflow implementation of this repo. Is there any plan to release tensorflow impl.?

If so, I will just wait your release. If not, I have the plan to implement it in long-term on my hands.

This wok is very nice. And thank you very much for release the codes. My issue is that I can not find orpooling in pytorch version and oralign in caffe version. Will you plan to release the corresponding codes in future? @ZhouYanzhao

First, thank you for this great contribution.

In your CVPR 2017 poster at Application 5 section there is a comparison of Faster R-CNN detection (bounding box only) with an ORN detection (bounding box + orientation). Could your elaborate on how you used ORN in a detection framework (maybe you have a github repo) and what dataset was used.

Hello,ZhouYanzhao.
when i input bash instal.sh, it shows error : orn/src/liborn_kernel.cu.o: 没有那个文件或目录. Can you help me? Thank you!

The instructions:

bash instal.sh ... ... ... build/temp.linux-x86_64-3.5/home/yangyu/ORN/ORN.pytorch/install/orn/src/liborn_cuda.o orn/src/liborn_kernel.cu.o -o build/lib.linux-x86_64-3.5/orn/_ext/liborn/_liborn.abi3.so x86_64-linux-gnu-gcc: error: orn/src/liborn_kernel.cu.o: 没有那个文件或目录

Exception: 'int' object does not support indexing, for line kernel = kernel_indices[kW][angle][j]

Wrapping the kernel_indices for size 1 as a list fixes the issue. New kernel_indices:

    kernel_indices = {
        1: {
            0: [1],
            45: [1],
            90: [1],
            135: [1],
            180: [1],
            225: [1],
            270: [1],
            315: [1]
        },
        3: {
            0: (1,2,3,4,5,6,7,8,9),
            45: (2,3,6,1,5,9,4,7,8),
            90: (3,6,9,2,5,8,1,4,7),
            135: (6,9,8,3,5,7,2,1,4),
            180: (9,8,7,6,5,4,3,2,1),
            225: (8,7,4,9,5,1,6,3,2),
            270: (7,4,1,8,5,2,9,6,3),
            315: (4,1,2,7,5,3,8,9,6)
        }
    }

note [Rot_ORN_8_Align] | epoch 197 | elapse 17.76 | remain  0: 0:53 | testLoss 0.046485 | testAcc 98.7000 | trainLoss 0.039256 | trainAcc 98.8201
note [Rot_ORN_8_Align] | epoch 198 | elapse 17.78 | remain  0: 0:35 | testLoss 0.056883 | testAcc 98.5600 | trainLoss 0.038547 | trainAcc 98.8722
note [Rot_ORN_8_Align] | epoch 199 | elapse 17.81 | remain  0: 0:17 | testLoss 0.068868 | testAcc 98.3600 | trainLoss 0.038445 | trainAcc 98.8421
note [Rot_ORN_8_Align] | epoch 200 | elapse 17.82 | remain  0: 0: 0 | testLoss 0.046446 | testAcc 98.6600 | trainLoss 0.041818 | trainAcc 98.7800
| Thu Jul 20 15:49:03 2017 | best model: /data/Repo/ORN.torch/demo/logs/Rot_ORN_8_Align/192-99.04-20170720154638.t7
{{MSG:| Thu Jul 20 15:49:03 2017 | [Rot_ORN_8_Align] all done. bestValidationAcc: 99.04, finalTestAcc: 99.02}}

Not as good as the paper presented.

• [x] added PyTorch implementation (including MNIST-Variants demo)
• [x] added Caffe implementation (including MNIST-Variants demo)
• [x] added the Poster for CVPR2017
• [x] added CIFAR performance of DenseNet to Table.1 for comparison

1. added more results of mean/std normalized CIFAR10/100.
2. added the performance of OR-ResNet-18 on ILSVRC2012 dataset.
3. released the imagenet pre-trained weights of OR-ResNet-18 model.
4. added a demo notebook of using imagenet pre-trained ORN to classify images.

Here is the official Torch implementation of ORN, which provides:

• [x] CPU/GPU/CuDNN versions
• [x] MNIST-Variants demo
• [x] OR-WideResNet model for CIFAR

Caffe and pyTorch implementation will come soon.

Dear author, I have read your paper recently, but I do not quite understand some details in it.It is ,why Norientation channels when had eight rotational versions.Looking forward to your answer.Thank you.

Sincerely Zhou, I appreciate your great work on oriented feature. Now I have a question that how you make the feature map visualization. so would you like to give me some advice?

Thanks for sharing!

Recently, I upgrade the pytorch branch to be compatible with pytorch 1.0. When I conduct the grad check for activerotaingfilter, I failed. But the rotationinvariantencoding success. I have checked my codes many times, but still don't solve it. Do you plan to upgrade this branch to python1.0?

I use the network defined in the demo.py, it is ok to reproduce the similar results for original --> rot experiments on mnist. But when I changed to or-resnet, it got very low results, the accuracy is no more than 50%(even lower than the simple OR-CNN defined in demo.py ). I do not know what's wrong with it. The following is my code. https://paste.ubuntu.com/p/NssKJbxDqZ/ What's more, when I do original --> rot experiments on cifar-10. I use the network defined in the demo.py. And got results of 35.26 and 44.97 for without/with orn respectively. Look forward to your reply.

At first I ran

python3 setup.py install 

which created build, dist and orn.egg-info folders. Inside the build folder there was the orn folder containing _ext which has liborn. But init.py file inside the liborn folder gives reference error on the line :

from ._liborn import lib as _lib, ffi as _ffi