Bumper Detection ConvNet ready for frame by frame analysis.

Talk about having the quietest Diwali (the festival of lights in India), the entire evening of mine was spent on the paper(s?) I’m working on, updating my resume and searching for procurements. Meanwhile, I also took some time out to finally complete the ConvNet and the code for classifying segments of a video live stream as bumpers and background. I studied few resources where similar classification was done, and I wanted my net to be a bit smaller than AlexNet because the classification was not THAT difficult.

pos-15
Sample Positive from the dataset
neg-51
Sample Negative from the dataset

I finally designed a network using an awesome tool I recently discovered, named ConvNet Designer. It lets you add Convolution, Pooling, Linear, ReLU, Fully connected and Output layers using a GUI and finally rolls out a TensorFlow code for the same. Although I was specifically using Torch, the overall experience of using the tool was fantastic and saved me a lot of calculation time.

I’d add the screenshot of the net and it’s result on a full size image by tomorrow.

View of the designed Net. The input is a 256x256x3 image (3 is the channel count)
View of the designed Net. The input is a 256x256x3 image (3 is the channel count)

I had some issues with CUDA versions and forwarding the image as a CudaTensor. But in the end, I managed to get a 99.2% test case accuracy on the test set by the fifth epoch. The overall net was quite fast (more than 100 tests per second) and was generalizing well.

With the control systems research, the motor still remains doubtful, and hence we have to wait for the repaired motor. Once it’s here, I guess it won’t be too long a job. I really feel I should be able to complete it by the end of this week.

The link to the dataset.

Happy Diwali. Maybe.

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