<p>We are getting there, with as usual some trade-off between quality and speed.</p>
<p>For example <a href="http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf" rel="nofollow noreferrer">Lecture 8: Spatial Localization and Detection lecture</a> shows some benchmarks (mAP = Mean Average Precision, higher is better; FPS = frame per second):</p>
<p><a href="https://i.sstatic.net/AfHt2m.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/AfHt2m.png" alt="Table/Performance: Real-Time Detectors" /></a></p>
<p><a href="https://i.sstatic.net/B5Qb9m.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/B5Qb9m.png" alt="Table/Performance: R-CNN, Fast R-CNN, Faster R-CNN" /></a></p>

CC BY-SA 4.0
2016-08-08T04:14:49.693Z
2020-11-12T21:30:17.417Z
2020-11-12T21:30:17.417Z
Franck Dernoncourt
<p>Convolutional neural network are leading type of feed-forward artificial neural network for image recognition. Can they be used for real-time image recognition for videos (frame by frame), or it takes too much processing (assuming they're written in C-like language)?</p>

<p>For example for classification of type of animals based on the training from huge dataset.</p>

<convolutional-neural-networks><classification><performance><real-time>
Can ConvNets be used for real-time object recognition from video feed?
kenorb
# Can ConvNets be used for real-time object recognition from video feed?

(Asked by: username_0 on 2016-08-07)

Convolutional neural network are leading type of feed-forward artificial neural network for image recognition. Can they be used for real-time image recognition for videos (frame by frame), or it takes too much processing (assuming they're written in C-like language)?

For example for classification of type of animals based on the training from huge dataset.

**Tags:** convolutional-neural-networks, classification, performance, real-time


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## 1 answers

### Answer by username_1 on 2016-08-08. Score: 2 | Accepted answer

We are getting there, with as usual some trade-off between quality and speed.

For example [Lecture 8: Spatial Localization and Detection lecture](http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf) shows some benchmarks (mAP = Mean Average Precision, higher is better; FPS = frame per second):

[![Table/Performance: Real-Time Detectors](https://i.sstatic.net/AfHt2m.png)](https://i.sstatic.net/AfHt2m.png)

[![Table/Performance: R-CNN, Fast R-CNN, Faster R-CNN](https://i.sstatic.net/B5Qb9m.png)](https://i.sstatic.net/B5Qb9m.png)


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