original faster-rcnn

.gitattributes

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+*.caffemodel filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text

.gitignore

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+*.pyc
+.ipynb_checkpoints
+lib/build
+lib/pycocotools/_mask.c
+lib/pycocotools/_mask.so

.gitmodules

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+[submodule "caffe-fast-rcnn"]
+	path = caffe-fast-rcnn
+	url = https://github.com/rbgirshick/caffe-fast-rcnn.git
+	branch = fast-rcnn

LICENSE

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+Faster R-CNN
+
+The MIT License (MIT)
+
+Copyright (c) 2015 Microsoft Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
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+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
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+
+************************************************************************
+
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+
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+
+1.	Caffe, (https://github.com/BVLC/caffe/)
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README.md

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+# py-faster-rcnn has been deprecated. Please see [Detectron](https://github.com/facebookresearch/Detectron), which includes an implementation of [Mask R-CNN](https://arxiv.org/abs/1703.06870).
+
+### Disclaimer
+
+The official Faster R-CNN code (written in MATLAB) is available [here](https://github.com/ShaoqingRen/faster_rcnn).
+If your goal is to reproduce the results in our NIPS 2015 paper, please use the [official code](https://github.com/ShaoqingRen/faster_rcnn).
+
+This repository contains a Python *reimplementation* of the MATLAB code.
+This Python implementation is built on a fork of [Fast R-CNN](https://github.com/rbgirshick/fast-rcnn).
+There are slight differences between the two implementations.
+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](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more information
+
+# *Faster* R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
+
+By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (Microsoft Research)
+
+This Python implementation contains contributions from Sean Bell (Cornell) written during an MSR internship.
+
+Please see the official [README.md](https://github.com/ShaoqingRen/faster_rcnn/blob/master/README.md) for more details.
+
+Faster R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/1506.01497) and was subsequently published in NIPS 2015.
+
+### License
+
+Faster R-CNN is released under the MIT License (refer to the LICENSE file for details).
+
+### Citing Faster R-CNN
+
+If you find Faster R-CNN useful in your research, please consider citing:
+
+    @inproceedings{renNIPS15fasterrcnn,
+        Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
+        Title = {Faster {R-CNN}: Towards Real-Time Object Detection
+                 with Region Proposal Networks},
+        Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
+        Year = {2015}
+    }
+
+### Contents
+1. [Requirements: software](#requirements-software)
+2. [Requirements: hardware](#requirements-hardware)
+3. [Basic installation](#installation-sufficient-for-the-demo)
+4. [Demo](#demo)
+5. [Beyond the demo: training and testing](#beyond-the-demo-installation-for-training-and-testing-models)
+6. [Usage](#usage)
+
+### Requirements: software
+
+**NOTE** If you are having issues compiling and you are using a recent version of CUDA/cuDNN, please consult [this issue](https://github.com/rbgirshick/py-faster-rcnn/issues/509?_pjax=%23js-repo-pjax-container#issuecomment-284133868) for a workaround
+
+1. Requirements for `Caffe` and `pycaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html))
+
+  **Note:** Caffe *must* be built with support for Python layers!
+
+  ```make
+  # In your Makefile.config, make sure to have this line uncommented
+  WITH_PYTHON_LAYER := 1
+  # Unrelatedly, it's also recommended that you use CUDNN
+  USE_CUDNN := 1
+  ```
+
+  You can download my [Makefile.config](https://dl.dropboxusercontent.com/s/6joa55k64xo2h68/Makefile.config?dl=0) for reference.
+2. Python packages you might not have: `cython`, `python-opencv`, `easydict`
+3. [Optional] MATLAB is required for **official** PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.
+
+### Requirements: hardware
+
+1. For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices
+2. For training Fast R-CNN with VGG16, you'll need a K40 (~11G of memory)
+3. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)
+
+### Installation (sufficient for the demo)
+
+1. Clone the Faster R-CNN repository
+  ```Shell
+  # Make sure to clone with --recursive
+  git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git
+  ```
+
+2. We'll call the directory that you cloned Faster R-CNN into `FRCN_ROOT`
+
+   *Ignore notes 1 and 2 if you followed step 1 above.*
+
+   **Note 1:** If you didn't clone Faster R-CNN with the `--recursive` flag, then you'll need to manually clone the `caffe-fast-rcnn` submodule:
+    ```Shell
+    git submodule update --init --recursive
+    ```
+    **Note 2:** The `caffe-fast-rcnn` submodule needs to be on the `faster-rcnn` branch (or equivalent detached state). This will happen automatically *if you followed step 1 instructions*.
+
+3. Build the Cython modules
+    ```Shell
+    cd $FRCN_ROOT/lib
+    make
+    ```
+
+4. Build Caffe and pycaffe
+    ```Shell
+    cd $FRCN_ROOT/caffe-fast-rcnn
+    # Now follow the Caffe installation instructions here:
+    #   http://caffe.berkeleyvision.org/installation.html
+
+    # If you're experienced with Caffe and have all of the requirements installed
+    # and your Makefile.config in place, then simply do:
+    make -j8 && make pycaffe
+    ```
+
+5. Download pre-computed Faster R-CNN detectors
+    ```Shell
+    cd $FRCN_ROOT
+    ./data/scripts/fetch_faster_rcnn_models.sh
+    ```
+
+    This will populate the `$FRCN_ROOT/data` folder with `faster_rcnn_models`. See `data/README.md` for details.
+    These models were trained on VOC 2007 trainval.
+
+### Demo
+
+*After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo.
+
+To run the demo
+```Shell
+cd $FRCN_ROOT
+./tools/demo.py
+```
+The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.
+
+### Beyond the demo: installation for training and testing models
+1. Download the training, validation, test data and VOCdevkit
+
+	```Shell
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
+	```
+
+2. Extract all of these tars into one directory named `VOCdevkit`
+
+	```Shell
+	tar xvf VOCtrainval_06-Nov-2007.tar
+	tar xvf VOCtest_06-Nov-2007.tar
+	tar xvf VOCdevkit_08-Jun-2007.tar
+	```
+
+3. It should have this basic structure
+
+	```Shell
+  	$VOCdevkit/                           # development kit
+  	$VOCdevkit/VOCcode/                   # VOC utility code
+  	$VOCdevkit/VOC2007                    # image sets, annotations, etc.
+  	# ... and several other directories ...
+  	```
+
+4. Create symlinks for the PASCAL VOC dataset
+
+	```Shell
+    cd $FRCN_ROOT/data
+    ln -s $VOCdevkit VOCdevkit2007
+    ```
+    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. [Optional] If you want to use COCO, please see some notes under `data/README.md`
+7. Follow the next sections to download pre-trained ImageNet models
+
+### Download pre-trained ImageNet models
+
+Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16.
+
+```Shell
+cd $FRCN_ROOT
+./data/scripts/fetch_imagenet_models.sh
+```
+VGG16 comes from the [Caffe Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo), but is provided here for your convenience.
+ZF was trained at MSRA.
+
+### Usage
+
+To train and test a Faster R-CNN detector using the **alternating optimization** algorithm from our NIPS 2015 paper, use `experiments/scripts/faster_rcnn_alt_opt.sh`.
+Output is written underneath `$FRCN_ROOT/output`.
+
+```Shell
+cd $FRCN_ROOT
+./experiments/scripts/faster_rcnn_alt_opt.sh [GPU_ID] [NET] [--set ...]
+# GPU_ID is the GPU you want to train on
+# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use
+# --set ... allows you to specify fast_rcnn.config options, e.g.
+#   --set EXP_DIR seed_rng1701 RNG_SEED 1701
+```
+
+("alt opt" refers to the alternating optimization training algorithm described in the NIPS paper.)
+
+To train and test a Faster R-CNN detector using the **approximate joint training** method, use `experiments/scripts/faster_rcnn_end2end.sh`.
+Output is written underneath `$FRCN_ROOT/output`.
+
+```Shell
+cd $FRCN_ROOT
+./experiments/scripts/faster_rcnn_end2end.sh [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](https://www.dropbox.com/s/xtr4yd4i5e0vw8g/iccv15_tutorial_training_rbg.pdf?dl=0) for more details.
+
+Artifacts generated by the scripts in `tools` are written in this directory.
+
+Trained Fast R-CNN networks are saved under:
+
+```
+output/<experiment directory>/<dataset name>/
+```
+
+Test outputs are saved under:
+
+```
+output/<experiment directory>/<dataset name>/<network snapshot name>/
+```