@@ -4,16 +4,18 @@ Created by Ross Girshick at Microsoft Research, Redmond.
### Introduction
*Fast R-CNN* is a clean and fast framework for object detection.
Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single fine-tuning run.
The multi-task loss simplifies and speeds up training.
Unlike SPPnet, all network layers can be learned during fine-tuning.
We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are fine-tuned.
Compared to "slow" R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster for detection, and achieves a significantly higher mAP on PASCAL VOC 2012.
Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate.
**Fast R-CNN** is a fast framework for object detection with deep ConvNets. Fast R-CNN is
- written in Python and C++/Caffe,
- trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than SPPnet,
- runs 200x faster than R-CNN and 10x faster than SPPnet at test-time,
- and has a significantly higher mAP on PASCAL VOC than both R-CNN and SPPnet.
Fast R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/todo).
### License
Fast R-CNN is released under the MIT License (refer to the LICENSE file for details).
### Citing Fast R-CNN
If you find R-CNN useful in your research, please consider citing:
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@@ -25,10 +27,6 @@ If you find R-CNN useful in your research, please consider citing:
Year = {2015}
}
### License
Fast R-CNN is released under the MIT License (refer to the LICENSE file for details).
### Installation requirements
1. Requirements for Caffe and pycaffe (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html))
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@@ -76,7 +74,7 @@ To run the demo
cd $FRCN_ROOT
./tools/demo.py
```
The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2012. The object proposals are pre-computed in order to reduce installation requirements.
The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. The object proposals are pre-computed in order to reduce installation requirements.
### Beyond the demo: installation for training and testing models
1. Download the training, validation, test data and VOCdevkit
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@@ -104,23 +102,15 @@ The demo performs detection using a VGG16 network trained for detection on PASCA
# ... and several other directories ...
```
4. Establish symlinks for the PASCAL VOC dataset
4. Create symlinks for the PASCAL VOC dataset
```Shell
cd $FRCN_ROOT/data
ln -s $VOCdevkit VOCdevkit2007
```
5. Establish a symlink for your cache directory
```Shell
cd $FRCN_ROOT/data
# /your/cache/path needs to be a directory that will hold a few GB of data
ln -s /your/cache/path cache
```
6. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012
7. Follow the next sections to download pre-computed object proposals and pre-trained ImageNet models
Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.
5. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012
6. Follow the next sections to download pre-computed object proposals and pre-trained ImageNet models
