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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Tensorflow Faster RCNN for Object Detection
| Date | Stars |
|---|---|
| 2026-07-24 | 3648 |
| 2026-07-25 | 3648 |
| 2026-07-28 | 3648 |
| 2026-07-30 | 3648 |
| 2026-08-06 | 3648 |
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# tf-faster-rcnn is deprecated: For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack [here](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN). # tf-faster-rcnn A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen ([email protected]). This repository is based on the python Caffe implementation of faster RCNN available [here](https://github.com/rbgirshick/py-faster-rcnn). **Note**: Several minor modifications are made when reimplementing the framework, which give potential improvements. For details about the modifications and ablative analysis, please refer to the technical report [An Implementation of Faster RCNN with Study for Region Sampling](https://arxiv.org/pdf/1702.02138.pdf). If you are seeking to reproduce the results in the original paper, please use the [official code](https://github.com/ShaoqingRen/faster_rcnn) or maybe the [semi-official code](https://github.com/rbgirshick/py-faster-rcnn). For details about the faster RCNN architecture please refer to the paper [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/pdf/1506.01497.pdf). ### Detection Performance The current code supports **VGG16**, **Resnet V1** and **Mobilenet V1** models. We mainly tested it on plain VGG16 and Resnet101 (thank you @philokey!) architecture. As the baseline, we report numbers using a single model on a single convolution layer, so no multi-scale, no multi-stage bounding box regression, no skip-connection, no extra input is used. The only data augmentation technique is left-right flipping during training following the original Faster RCNN. All models are released. With VGG16 (``conv5_3``): - Train on VOC 2007 trainval and test on VOC 2007 test, **70.8**. - Train on VOC 2007+2012 trainval and test on VOC 2007 test ([R-FCN](https://github.com/daijifeng001/R-FCN) schedule), **75.7**. - Train on COCO 2014 [trainval35k](https://github.com/rbgirshick/py-faster-rcnn/tree/master/models) and test on [minival](https://github.com/rbgirshick/py-faster-rcnn/tree/master/models) (*Iterations*: 900k/1190k), **30.2**. With Resnet101 (last ``conv4``): - Train on VOC 2007 trainval and test on VOC 2007 test, **75.7**. - Train on VOC 2007+2012 trainval and test on VOC 2007 test (R-FCN schedule), **79.8**. - Train on COCO 2014 trainval35k and test on minival (900k/1190k), **35.4**. More Results: - Train Mobilenet (1.0, 224) on COCO 2014 trainval35k and test on minival (900k/1190k), **21.8**. - Train Resnet50 on COCO 2014 trainval35k and test on minival (900k/1190k), **32.4**. - Train Resnet152 on COCO 2014 trainval35k and test on minival (900k/1190k), **36.1**. Approximate *baseline* [setup](https://github.com/endernewton/tf-faster-rcnn/blob/master/experiments/cfgs/res101-lg.yml) from [FPN](https://arxiv.org/abs/1612.03144) (this repository does not contain training code for FPN yet): - Train Resnet50 on COCO 2014 trainval35k and test on minival (900k/1190k), **34.2**. - Train Resnet101 on COCO 2014 trainval35k and test on minival (900k/1190k), **37.4**. - Train Resnet152 on COCO 2014 trainval35k and test on minival (900k/1190k), **38.2**. **Note**: - Due to the randomness in GPU training with Tensorflow especially for VOC, the best numbers are reported (with 2-3 attempts) here. According to my experience, for COCO you can almost always get a very close number (within ~0.2%) despite the randomness. - The numbers are obtained with the **default** testing scheme which selects region proposals using non-maximal suppression (TEST.MODE nms), the alternative testing scheme (TEST.MODE top) will likely result in slightly better performance (see [report](https://arxiv.org/pdf/1702.02138.pdf), for COCO it boosts 0.X AP). - Since we keep the small proposals (\< 16 pixels width/height), our performance is especially good for small objects. - We do not set a thresho
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:03be0bae4839e2a4, topic:object-detection, desc:object detection, readme:object detection
matched fp:03be0bae4839e2a4, topic:tensorflow
