Improving the framework for 2D detection

[Gautam Malu]

Hi,

I am a graduate student working in computer-vision at Internation Institute of Information Technology Hyderabad. I am applying for GSOC 2017 for this library. I want to work on CNNs for object detection idea. I have installed the library and tested out examples.
I have prepared a brief report on current state of art CNN methods for 2D object detections  https://github.com/gautamMalu/opendetection/blob/algo_report/ObjectDetection.md

I have following queries regarding this idea:
On the project page, it is mentioned that

 

• Implement OD interface for training and testing (under od::ODTrainer s) neural network architectures with convolutional layer and possible recurrent layers using Caffe.

Is there a particular reason for using Caffe, I am asking because Tensorflow also have C++ API, and it's better maintained than Caffe. 

• Add to OpenDitection the following algorithms: .. 1. Classification - Types: different options - AlexNet, VGG, Resnet50, Resnet150, GoogLenet. .. 2. Detection - Faster RCNN .. 3. Location probability map

OD is for object detection so why are focusing on Classification instead of detection?

[Shyam Nandan Rai]

Hi Gautam,

I think the main idea of implementing classification is use of probability location map.As this method localizes the object using classification (though we use last convlution layer for this method)

For more details please refer the link : https://arxiv.org/abs/1311.2901

[Gautam Malu]

Hi Shyam,

Thanks for the clarification. If that is the idea behind the classification, we can use these state of the art gradient based visualization techniques:

http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf
https://arxiv.org/abs/1610.02391

You can find the implementation of 2nd paper for VGG16 and ResNet50 here (https://github.com/gautamMalu/keras-grad-cam)

However, I am still not so clear why we are using these unsupervised localization techniques instead of state of the art supervised techniques such as faster-rcnn, YOLO. I guess data preparation for the training of these techniques is little challenging but we can start with providing the pre-trained models as detectors first and then figure out the training of these models also.

Best regards,
Gautam Malu

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[Shyam Nandan Rai]

Hi Gautam,

Yes, you are right. If we start training for faster r-cnn it takes high computation resources (at least 3gb for training on small dataset). So, it would be better to start with pre- trained models.

[Gautam Malu]

Hi,

I have implemented grad-CAM visualization technique (https://arxiv.org/abs/1610.02391) for Caffe too https://github.com/gautamMalu/caffe-gradCAM

Best regards,
Gautam Malu

On Wed, Apr 5, 2017 at 4:34 PM, Shyam Nandan Rai <shya...@iiits.in> wrote:

Hi Gautam,

Yes, you are right. If we start training for faster r-cnn it takes high computation resources (at least 3gb for training on small dataset). So, it would be better to start with pre- trained models.

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