rightsForRobots

This is the best I got. You'll have to imagine what the video was:

https://github.com/ryanjay0/miles-deep/raw/master/images/pre...

Yes even within a particular video there are lots of frames where the act is implied not directly shown, like a close-up of others faces. Karpathy et al. showed they could still learn from the sports video database even with random crowd shots or announcer shots not being removed.

I think the quality for the data influences the result and hand crafting the dataset is what lead to 95% accuracy on new instances.

Good question. There's an example specifically using caffenet to find cats. Any caffe model can be dropped in without recompiling.

Tried again. Better: http://i.imgur.com/ALoLmcX.jpg

This program can classify 1 hour of video in 36 seconds on my low end GTX 960 4GB.

I think thats doable. I'll be adding an autotag mode. I've been thinking about other attributes I can detect from race, to hair color, to number of participants.

Crowd sourcing is good except it can't tag new videos no one's seen yet.

This program can also be viewed as a general framework for classifying video with a Caffe model, using batching and threading in C++. By replacing the weights, model definition, and mean file it can immediately be used to edit videos with other classes without recompiling

I don't have the rights to the dataset, so unfortunately I won't be releasing it.

I actually tried that. It's not as interesting as you'd think. Perhaps having an 'other' category makes it more difficult.

It's trivial to drop the model into the deepdream ipython notebook they provide: https://github.com/google/deepdream/blob/master/dream.ipynb

Good point. The project mentions this work which did experiments on sports: http://cs.stanford.edu/people/karpathy/deepvideo/

The photos aren't available but the trained model is. It's a set of weights.