Top AI Repos — open-source AI, indexed and scored
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.
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.
FreeAnchor: Learning to Match Anchors for Visual Object Detection (NeurIPS 2019)
| Date | Stars |
|---|---|
| 2026-07-24 | 669 |
| 2026-07-25 | 669 |
| 2026-07-28 | 669 |
| 2026-07-30 | 668 |
| 2026-08-06 | 668 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
## FreeAnchor
The Code for ["FreeAnchor: Learning to Match Anchors for Visual Object Detection"](https://arxiv.org/abs/1909.02466).
This repository is based on maskrcnn-benchmark, and FreeAnchor has also been implemented in [mmdetection](https://github.com/open-mmlab/mmdetection), thanks [@yhcao6](https://github.com/yhcao6) and [@hellock](https://github.com/hellock).
### New performance on COCO
We added multi-scale testing support and updated experiments. The previous version is in [this branch](https://github.com/zhangxiaosong18/FreeAnchor/tree/previous).
| Backbone | Iteration | Training scales | Multi-scale<br>testing | AP<br>(minival) | AP<br>(test-dev) | Model |
| :-------------------: | :-------: | :-------------: | :--------------: | :-------------: | :--------------: | :--------: |
| ResNet-50-FPN | 90k | 800 | N | 38.7 | 38.7 | [Link](https://drive.google.com/open?id=1o-VvpOIwYCPxyas8n6OngpOznlVy0T6k ) |
| ResNet-101-FPN | 90k | 800 | N | 40.5 | 40.9 | [Link](https://drive.google.com/open?id=1jc5ncxuuuG3-sm-4OpkOfr51ClwCWHu0 ) |
| ResNet-101-FPN | 180k | [640, 800] | N | 42.7 | 43.1 | [Link](https://drive.google.com/open?id=1OvK8Xona8v7mWU2nf5Fp1QzvwUaFhlIg ) |
| ResNet-101-FPN | 180k | [480, 960] | N | 43.2 | 43.9 | [Link](https://drive.google.com/open?id=1ZIx2HTexVyU6xTwAm2ABTYjsJuLvlfhB ) |
| ResNet-101-FPN | 180k | [480, 960] | Y | 44.7 | 45.2 | [Link](https://drive.google.com/open?id=1ZIx2HTexVyU6xTwAm2ABTYjsJuLvlfhB ) |
| ResNeXt-64x4d-101-FPN | 180k | [640, 800] | N | 44.5 | 44.9 | [Link](https://drive.google.com/open?id=1MrtXoBGHceq_BBY5cH-kw2ax5-aDPdTv ) |
| ResNeXt-64x4d-101-FPN | 180k | [480, 960] | N | 45.6 | 46.0 | [Link](https://drive.google.com/open?id=1r17agiu76xtwKxn2oE_pK4R847k-Cu5m ) |
| ResNeXt-64x4d-101-FPN | 180k | [480, 960] | Y | 46.8 | 47.3 | [Link](https://drive.google.com/open?id=1r17agiu76xtwKxn2oE_pK4R847k-Cu5m ) |
**Notes:**
- We use 8 GPUs with 2 image / GPU.
- In multi-scale testing, we use image scales in {480, 640, 800, 960, 1120, 1280} and max_size are 1.666× than scales.
## Installation
Check [INSTALL.md](INSTALL.md) for installation instructions.
## Usage
You will need to download the COCO dataset and configure your own paths to the datasets.
For that, all you need to do is to modify `maskrcnn_benchmark/config/paths_catalog.py` to point to the location where your dataset is stored.
#### Config Files
We provide four configuration files in the configs directory.
| Config File | Backbone | Iteration | Training scales |
| :---------------------------------------: | :---------------------: | :-------: | :-------------: |
| configs/free_anchor_R-50-FPN_1x.yaml | ResNet-50-FPN | 90k | 800 |
| configs/free_anchor_R-101-FPN_1x.yaml | ResNet-101-FPN | 90k | 800 |
| configs/free_anchor_R-101-FPN_j2x.yaml | ResNet-101-FPN | 180k | [640, 800] |
| configs/free_anchor_X-101-FPN_j2x.yaml | ResNeXt-64x4d-101-FPN | 180k | [640, 800] |
| configsExcerpt of 5,515 characters
Read on GitHub
82
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:48b05120a857c82d, topic:computer-vision, topic:object-detection, desc:object detection