R-CNN

Compared to the generic traditional pipeline, the feature extraction is performed using a CNN. This way:

  1. Selective Search produces 2,000 region proposals

  2. The CNN model extracts a 4,096 vector for each region.

  3. An SVM classifies the region into background or object classes.

Drawbacks:

  1. Multi-stage model -> cannot be trained end-2-end.

  2. Uses disk caching of the extracted features -> exponential growth of disk usage.

  3. Selective Search is slow.

  4. The CNN is run on each independent region -> very slow

PreviousTraditional Detection ModelsNextFast R-CNN

Last updated