人工智能大作业-无人机图像目标检测.zip

# High quality, fast, modular reference implementation of SSD in PyTorch 1.0 This repository implements [SSD (Single Shot MultiBox Detector)](https://arxiv.org/abs/1512.02325). The implementation is heavily influenced by the projects [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch), [pytorch-ssd](https://github.com/qfgaohao/pytorch-ssd) and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). This repository aims to be the code base for researches based on SSD. <div align="center"> <img src="figures/004545.jpg" width="500px" /> <p>Example SSD output (vgg_ssd300_voc0712).</p> </div> | Losses | Learning rate | Metrics | | :-----------: |:-------------:| :------:| |  |  |  | ## Highlights - **PyTorch 1.0**: Support PyTorch 1.0 or higher. - **Multi-GPU training and inference**: We use `DistributedDataParallel`, you can train or test with arbitrary GPU(s), the training schema will change accordingly. - **Modular**: Add your own modules without pain. We abstract `backbone`,`Detector`, `BoxHead`, `BoxPredictor`, etc. You can replace every component with your own code without change the code base. For example, You can add [EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch) as backbone, just add `efficient_net.py` (ALREADY ADDED) and register it, specific it in the config file, It's done! - **CPU support for inference**: runs on CPU in inference time. - **Smooth and enjoyable training procedure**: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training `CMD`. - **Batched inference**: can perform inference using multiple images per batch per GPU. - **Evaluating during training**: eval you model every `eval_step` to check performance improving or not. - **Metrics Visualization**: visualize metrics details in tensorboard, like AP, APl, APm and APs for COCO dataset or mAP and 20 categories' AP for VOC dataset. - **Auto download**: load pre-trained weights from URL and cache it. ## Installation ### Requirements 1. Python3 1. PyTorch 1.0 or higher 1. yacs 1. [Vizer](https://github.com/lufficc/Vizer) 1. GCC >= 4.9 1. OpenCV ### Step-by-step installation ```bash git clone https://github.com/lufficc/SSD.git cd SSD #Required packages pip install torch torchvision yacs tqdm opencv-python vizer # Optional packages # If you want visualize loss curve. Default is enabled. Disable by using --use_tensorboard 0 when training. pip install tensorboardX # If you train coco dataset, must install cocoapi. cd ~/github git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python setup.py build_ext install ``` ### Build If your torchvision >= 0.3.0, nms build is not needed! We also provide a python-like nms, but is very slower than build-version. ```bash # For faster inference you need to build nms, this is needed when evaluating. Only training doesn't need this. cd ext python build.py build_ext develop ``` ## Train ### Setting Up Datasets #### Pascal VOC For Pascal VOC dataset, make the folder structure like this: ``` VOC_ROOT |__ VOC2007 |_ JPEGImages |_ Annotations |_ ImageSets |_ SegmentationClass |__ VOC2012 |_ JPEGImages |_ Annotations |_ ImageSets |_ SegmentationClass |__ ... ``` Where `VOC_ROOT` default is `datasets` folder in current project, you can create symlinks to `datasets` or `export VOC_ROOT="/path/to/voc_root"`. #### COCO For COCO dataset, make the folder structure like this: ``` COCO_ROOT |__ annotations |_ instances_valminusminival2014.json |_ instances_minival2014.json |_ instances_train2014.json |_ instances_val2014.json |_ ... |__ train2014 |_ <im-1-name>.jpg |_ ... |_ <im-N-name>.jpg |__ val2014 |_ <im-1-name>.jpg |_ ... |_ <im-N-name>.jpg |__ ... ``` Where `COCO_ROOT` default is `datasets` folder in current project, you can create symlinks to `datasets` or `export COCO_ROOT="/path/to/coco_root"`. ### Single GPU training ```bash # for example, train SSD300: python train.py --config-file configs/vgg_ssd300_voc0712.yaml ``` ### Multi-GPU training ```bash # for example, train SSD300 with 4 GPUs: export NGPUS=4 python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --config-file configs/vgg_ssd300_voc0712.yaml SOLVER.WARMUP_FACTOR 0.03333 SOLVER.WARMUP_ITERS 1000 ``` The configuration files that I provide assume that we are running on single GPU. When changing number of GPUs, hyper-parameter (lr, max_iter, ...) will also changed according to this paper: [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677). ## Evaluate ### Single GPU evaluating ```bash # for example, evaluate SSD300: python test.py --config-file configs/vgg_ssd300_voc0712.yaml ``` ### Multi-GPU evaluating ```bash # for example, evaluate SSD300 with 4 GPUs: export NGPUS=4 python -m torch.distributed.launch --nproc_per_node=$NGPUS test.py --config-file configs/vgg_ssd300_voc0712.yaml ``` ## Demo Predicting image in a folder is simple: ```bash python demo.py --config-file configs/vgg_ssd300_voc0712.yaml --images_dir demo --ckpt https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_voc0712.pth ``` Then it will download and cache `vgg_ssd300_voc0712.pth` automatically and predicted images with boxes, scores and label names will saved to `demo/result` folder by default. You will see a similar output: ```text (0001/0005) 004101.jpg: objects 01 | load 010ms | inference 033ms | FPS 31 (0002/0005) 003123.jpg: objects 05 | load 009ms | inference 019ms | FPS 53 (0003/0005) 000342.jpg: objects 02 | load 009ms | inference 019ms | FPS 51 (0004/0005) 008591.jpg: objects 02 | load 008ms | inference 020ms | FPS 50 (0005/0005) 000542.jpg: objects 01 | load 011ms | inference 019ms | FPS 53 ``` ## MODEL ZOO ### Origin Paper: | | VOC2007 test | coco test-dev2015 | | :-----: | :----------: | :----------: | | SSD300* | 77.2 | 25.1 | | SSD512* | 79.8 | 28.8 | ### COCO: | Backbone | Input Size | box AP | Model Size | Download | | :------------: | :----------:| :--------------------------: | :--------: | :-------: | | VGG16 | 300 | 25.2 | 262MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_coco_trainval35k.pth) | | VGG16 | 512 | 29.0 | 275MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd512_coco_trainval35k.pth) | ### PASCAL VOC: | Backbone | Input Size | mAP | Model Size | Download | | :--------------: | :----------:| :--------------------------: | :--------: | :-------: | | VGG16 | 300 | 77.7 | 201MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd300_voc0712.pth) | | VGG16 | 512 | 80.7 | 207MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/vgg_ssd512_voc0712.pth) | | Mobilenet V2 | 320 | 68.9 | 25.5MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/mobilenet_v2_ssd320_voc0712_v2.pth) | | EfficientNet-B3 | 300 | 73.9 | 97.1MB | [model](https://github.com/lufficc/SSD/releases/download/1.2/efficient_net_b3_ssd300_voc0712.pth) | ## Develop Guide If you want to add your custom components, please see [DEVELOP_GUIDE.md](DEVELOP_GUIDE.md) for more details. ## Troubleshooting If you have issues running or compiling this code, we have compiled a list of common issues in [TROUBLESHOOTING.md](TROUBLESHOOTING.md). If your issue is not present there, please feel free t