py-faster-rcnn: Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

Model Zoo

COCO Faster R-CNN VGG-16 trained using end-to-end

Model URL: https://dl.dropboxusercontent.com/s/cotx0y81zvbbhnt/coco_vgg16_faster_rcnn_final.caffemodel?dl=0

Training command:

tools/train_net.py \
    --gpu 0 \
    --solver ./models/coco/VGG16/faster_rcnn_end2end/solver.prototxt \
    --weights data/imagenet_models/VGG16.v2.caffemodel \
    --imdb coco_2014_train+coco_2014_valminusminival \
    --iters 490000 \
    --cfg ./experiments/cfgs/faster_rcnn_end2end.yml

py-faster-rcnn commit: 68eec95

test-dev2015 results

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.242
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.453
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.235
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.371
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.238
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.340
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.346
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.385
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.544

test-standard2015 results

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.242
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.453
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.234
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.072
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.264
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.369
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.238
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.341
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.347
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.389
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.544

Rukit/py-faster-rcnn

Model Zoo

COCO Faster R-CNN VGG-16 trained using end-to-end

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Model Zoo

COCO Faster R-CNN VGG-16 trained using end-to-end

简介

发行版

贡献者

近期动态

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Rukit/py-faster-rcnn