Awesome
The repo contains our code for VisDA 2020 challenge
What's new
Requirement
- pytorch>=1.2.0
- yacs
- sklearn
- apex
- faiss (pip install faiss-gpu)
Reproduce results on VisDA 2020 Challenge
Refer to VISDA20.md and tech_report,
trained models can be download from here
- leaderboard (ranged by rank1)
| team | mAP | rank1 |
|---|
| vimar | 76.56% | 84.25% |
| xiangyu(ours) | 72.39% | 83.85% |
| yxge | 74.78% | 82.86% |
- Ablation on validation set
| method | mAP | rank1 |
|---|
| personx-spgan | 37.7% | 63.7% |
| +pseudo label | 51.8% | 77.7% |
| +BN finetune | 55.5% | 81.4% |
| +re-rank | 73.4% | 80.9% |
| +remove camera bias | 79.5% | 89.1% |
| ensemble | 82.7% | 90.7% |
Benchmark
Setting: ResNet50-ibn-a, single RTX 2080 Ti, FP16
| method | mAP | rank1 |
|---|
| bag-of-tricks | 88.2% | 95.0% |
| fast reid | 89.3% | 95.3% |
| ours | 88.4% | 95.1% |
| method | mAP | rank1 |
|---|
| bag-of-tricks | 79.1% | 90.1% |
| fast-reid | 81.2% | 90.8% |
| ours | 80.1% | 90.3% |
| method | mAP | rank1 |
|---|
| Bag of Tricks | 54.4% | 77.0% |
| fast reid | 60.6% | 83.9% |
| ours | 60.6% | 83.1% |