基于Faster-RCNN算法对目标进行识别与分类源码.zip

# Faster R-CNN in MXNet with distributed implementation and data parallelization  ## Why? There exist good implementations of Faster R-CNN yet they lack support for recent ConvNet architectures. The aim of reproducing it from scratch is to fully utilize MXNet engines and parallelization for object detection. | Indicator | py-faster-rcnn (caffe resp.) | mx-rcnn (this reproduction) | | :-------- | :--------------------------- | :-------------------------- | | Speed [1] | 2.5 img/s training, 5 img/s testing | 3.8 img/s in training, 12.5 img/s testing | | Performance [2] | mAP 73.2 | mAP 75.97 | | Efficiency [3] | 11G for Fast R-CNN | 4.6G for Fast R-CNN | | Parallelization [4] | None | 3.8 img/s to 6 img/s for 2 GPUs | | Extensibility [5] | Old framework and base networks | ResNet | [1] On Ubuntu 14.04.5 with device Titan X, cuDNN enabled. The experiment is VGG-16 end-to-end training. [2] VGG network. Trained end-to-end on VOC07trainval+12trainval, tested on VOC07 test. [3] VGG network. Fast R-CNN is the most memory expensive process. [4] VGG network (parallelization limited by bandwidth). ResNet-101 speeds up from 2 img/s to 3.5 img/s. [5] py-faster-rcnn does not support ResNet or recent caffe version. ## Why Not? * If you value stability and reproducibility over performance and efficiency, please refer to official implementations. There is no promise in all cases nor all experiments. * If you value simplicity. Technical details are *very complicated* in MXNet. This is by design to attain maximum possible performance instead of patching fixes after fixes. Performance and parallelization are more than a change of parameter. * If you want to do CPU training, be advised that it has not been verified yet. You will not encounter NOT_IMPLEMENTED_ERROR so it is still possible. * If you are on Windows or Python3, some people reported it was possible with some modifications. But they have disappeared. ## Experiments | Method | Network | Training Data | Testing Data | Reference | Result | Link | | :----- | :------ | :------------ | :----------- | :-------: | :----: | :---: | | Fast R-CNN | VGG16 | VOC07 | VOC07test | 66.9 | 66.50 | [Dropbox](https://www.dropbox.com/s/xmxjitv0kl96h7v/vgg_fast_rcnn-0008.params?dl=0) | | Faster R-CNN alternate | VGG16 | VOC07 | VOC07test | 69.9 | 69.62 | [Dropbox](https://www.dropbox.com/s/fgj71uzxz8h6ajj/vgg_voc_alter-0008.params?dl=0) | | Faster R-CNN end-to-end | VGG16 | VOC07 | VOC07test | 69.9 | 70.23 | [Dropbox](https://www.dropbox.com/s/gfxnf1qzzc0lzw2/vgg_voc07-0010.params?dl=0) | | Faster R-CNN end-to-end | VGG16 | VOC07+12 | VOC07test | 73.2 | 75.97 | [Dropbox](https://www.dropbox.com/s/rvktx65s48cuyb9/vgg_voc0712-0010.params?dl=0) | | Faster R-CNN end-to-end | ResNet-101 | VOC07+12 | VOC07test | 76.4 | 79.35 | [Dropbox](https://www.dropbox.com/s/ge2wl0tn47xezdf/resnet_voc0712-0010.params?dl=0) | | Faster R-CNN end-to-end | VGG16 | COCO train | COCO val | 21.2 | 22.8 | [Dropbox](https://www.dropbox.com/s/e0ivvrc4pku3vj7/vgg_coco-0010.params?dl=0) | | Faster R-CNN end-to-end | ResNet-101 | COCO train | COCO val | 27.2 | 26.1 | [Dropbox](https://www.dropbox.com/s/bfuy2uo1q1nwqjr/resnet_coco-0010.params?dl=0) | The above experiments were conducted at [a mx-rcnn version](https://github.com/precedenceguo/mx-rcnn/tree/6a1ab0eec5035a10a1efb5fc8c9d6c54e101b4d0) using [a MXNet fork, based on MXNet 0.9.1 nnvm pre-release](https://github.com/precedenceguo/mxnet/tree/simple). ## I'm Feeling Lucky * Prepare: `bash script/additional_deps.sh` * Download training data: `bash script/get_voc.sh` * Download pretrained model: `bash script/get_pretrained_model.sh` * Training and testing: `bash script/vgg_voc07.sh 0,1` (use gpu 0 and 1) ## Getting started See if `bash script/additional_deps.sh` will do the following for you. * Suppose `HOME` represents where this file is located. All commands, unless stated otherwise, should be started from `HOME`. * Install python package `cython easydict matplotlib scikit-image`. * Install MXNet version v0.9.5 or higher and MXNet Python Interface. Open `python` type `import mxnet` to confirm. * Run `make` in `HOME`. Command line arguments have the same meaning as in mxnet/example/image-classification. * `prefix` refers to the first part of a saved model file name and `epoch` refers to a number in this file name. In `model/vgg-0000.params`, `prefix` is `"model/vgg"` and `epoch` is `0`. * `begin_epoch` means the start of your training process, which will apply to all saved checkpoints. * Remember to turn off cudnn auto tune. `export MXNET_CUDNN_AUTOTUNE_DEFAULT=0`. ## Demo (Pascal VOC) * An example of trained model (trained on VOC07 trainval) can be accessed from [Baidu Yun](http://pan.baidu.com/s/1boRhGvH) (ixiw) or [Dropbox](https://www.dropbox.com/s/jrr83q0ai2ckltq/final-0000.params.tar.gz?dl=0). If you put the extracted model `final-0000.params` in `HOME` then use `--prefix final --epoch 0` to access it. * Try out detection result by running `python demo.py --prefix final --epoch 0 --image myimage.jpg --gpu 0 --vis`. Drop the `--vis` if you do not have a display or want to save as a new file. ## Training Faster R-CNN The following tutorial is based on VOC data, VGG network. Supply `--network resnet` and `--dataset coco` to use other networks and datasets. Refer to `script/vgg_voc07.sh` and other experiments for examples. ### Prepare Training Data See `bash script/get_voc.sh` and `bash script/get_coco.sh` will do the following for you. * Make a folder `data` in `HOME`. `data` folder will be used to place the training data folder `VOCdevkit` and `coco`. * Download and extract [Pascal VOC data](http://host.robots.ox.ac.uk/pascal/VOC/), place the `VOCdevkit` folder in `HOME/data`. * Download and extract [coco dataset](http://mscoco.org/dataset/), place all images to `coco/images` and annotation jsons to `data/annotations`. (Skip this if not interested) All dataset have three attributes, `image_set`, `root_path` and `dataset_path`. * `image_set` could be `2007_trainval` or something like `2007trainval+2012trainval`. * `root_path` is usually `data`, where `cache`, `selective_search_data`, `rpn_data` will be stored. * `dataset_path` could be something like `data/VOCdevkit`, where images, annotations and results can be put so that many copies of datasets can be linked to the same actual place. ### Prepare Pretrained Models See if `bash script/get_pretrained_model.sh` will do this for you. If not, * Make a folder `model` in `HOME`. `model` folder will be used to place model checkpoints along the training process. It is recommended to set `model` as a symbolic link to somewhere else in hard disk. * Download VGG16 pretrained model `vgg16-0000.params` from [MXNet model gallery](https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-vgg.md) to `model` folder. * Download ResNet pretrained model `resnet-101-0000.params` from [ResNet](https://github.com/tornadomeet/ResNet) to `model` folder. ### Alternate Training See if `bash script/vgg_alter_voc07.sh 0` (use gpu 0) will do the following for you. * Start training by running `python train_alternate.py`. This will train the VGG network on the VOC07 trainval. More control of training process can be found in the argparse help. * Start testing by running `python test.py --prefix model/final --epoch 0` after completing the training process. This will test the VGG network on the VOC07 test with the model in `HOME/model/final-0000.params`. Adding a `--vis` will turn on visualization and `-h` will show help as in the training process. ### End-to-end Training (approximate process) See if `bash script/vgg_voc07.sh 0` (use gpu 0) will do the following for you. * Start training by running `python train_