Commit f0ccb513 authored by Ross Girshick's avatar Ross Girshick

Document approximate joint / end-to-end training

parent 3343dbb1
......@@ -10,6 +10,7 @@ In particular, this Python port
- is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 220ms / image vs. 200ms / image for VGG16)
- gives similar, but not exactly the same, mAP as the MATLAB version
- is *not compatible* with models trained using the MATLAB code due to the minor implementation differences
- **includes approximate joint training** that is 1.5x faster than alternating optimization (for VGG16) -- see these [slides]( for more information
# *Faster* R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
......@@ -171,7 +172,7 @@ ZF was trained at MSRA.
### Usage
To train and test a Faster R-CNN detector use `experiments/scripts/`.
To train and test a Faster R-CNN detector using the **alternating optimization** algorithm from our NIPS 2015 paper, use `experiments/scripts/`.
Output is written underneath `$FRCN_ROOT/output`.
......@@ -184,3 +185,17 @@ cd $FRCN_ROOT
("alt opt" refers to the alternating optimization training algorithm described in the NIPS paper.)
To train and test a Faster R-CNN detector using the **approximate joint training** method, use `experiments/scripts/`.
Output is written underneath `$FRCN_ROOT/output`.
./experiments/scripts/ [GPU_ID] [NET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use
# --set ... allows you to specify fast_rcnn.config options, e.g.
# --set EXP_DIR seed_rng1701 RNG_SEED 1701
This method trains the RPN module jointly with the Fast R-CNN network, rather than alternating between training the two. It results in faster (~ 1.5x speedup) training times and similar detection accuracy. See these [slides]( for more details.
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