Faster-RCNNKITTI数据集上的车辆行人检测.zip

This directory holds (*after you download them*): - Caffe models pre-trained on ImageNet - Faster R-CNN models - Symlinks to datasets To download Caffe models (ZF, VGG16) pre-trained on ImageNet, run: ``` ./data/scripts/fetch_imagenet_models.sh ``` This script will populate `data/imagenet_models`. To download Faster R-CNN models trained on VOC 2007, run: ``` ./data/scripts/fetch_faster_rcnn_models.sh ``` This script will populate `data/faster_rcnn_models`. In order to train and test with PASCAL VOC, you will need to establish symlinks. From the `data` directory (`cd data`): ``` # For VOC 2007 ln -s /your/path/to/VOC2007/VOCdevkit VOCdevkit2007 # For VOC 2012 ln -s /your/path/to/VOC2012/VOCdevkit VOCdevkit2012 ``` Install the MS COCO dataset at /path/to/coco ``` ln -s /path/to/coco coco ``` For COCO with Fast R-CNN, place object proposals under `coco_proposals` (inside the `data` directory). You can obtain proposals on COCO from Jan Hosang at https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/how-good-are-detection-proposals-really/. For COCO, using MCG is recommended over selective search. MCG boxes can be downloaded from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/. Use the tool `lib/datasets/tools/mcg_munge.py` to convert the downloaded MCG data into the same file layout as those from Jan Hosang. Since you'll likely be experimenting with multiple installs of Fast/er R-CNN in parallel, you'll probably want to keep all of this data in a shared place and use symlinks. On my system I create the following symlinks inside `data`: Annotations for the 5k image 'minival' subset of COCO val2014 that I like to use can be found at http://www.cs.berkeley.edu/~rbg/faster-rcnn-data/instances_minival2014.json.zip. Annotations for COCO val2014 (set) minus minival (~35k images) can be found at http://www.cs.berkeley.edu/~rbg/faster-rcnn-data/instances_valminusminival2014.json.zip. ``` # data/cache holds various outputs created by the datasets package ln -s /data/fast_rcnn_shared/cache # move the imagenet_models to shared location and symlink to them ln -s /data/fast_rcnn_shared/imagenet_models # move the selective search data to a shared location and symlink to them # (only applicable to Fast R-CNN training) ln -s /data/fast_rcnn_shared/selective_search_data ln -s /data/VOC2007/VOCdevkit VOCdevkit2007 ln -s /data/VOC2012/VOCdevkit VOCdevkit2012 ```