FastReID Model Zoo and Baselines
Introduction
This file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA V100 GPU.
The software in use were PyTorch 1.6, CUDA 10.1.
In addition to these official baseline models, you can find more models in projects/.
How to Read the Tables
- The "Name" column contains a link to the config file.
Running tools/train_net.py
with this config file and 1 GPU will reproduce the model.
Common Settings for all Person reid models
BoT:
Bag of Tricks and A Strong Baseline for Deep Person Re-identification. CVPRW2019, Oral.
AGW:
ReID-Survey with a Powerful AGW Baseline.
MGN:
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
SBS:
stronger baseline on top of BoT:
Bag of Freebies(BoF):
- Circle loss
- Freeze backbone training
- Cutout data augmentation & Auto Augmentation
- Cosine annealing learning rate decay
- Soft margin triplet loss
Bag of Specials(BoS):
- Non-local block
- GeM pooling
Market1501 Baselines
BoT:
AGW:
SBS:
MGN:
Method |
Pretrained |
Rank@1 |
mAP |
mINP |
download |
SBS(R50-ibn) |
ImageNet |
95.8% |
89.8% |
67.7% |
model |
DukeMTMC Baseline
BoT:
AGW:
SBS:
MGN:
Method |
Pretrained |
Rank@1 |
mAP |
mINP |
download |
SBS(R50-ibn) |
ImageNet |
91.1% |
82.0% |
46.8% |
model |
MSMT17 Baseline
BoT:
AGW:
SBS:
MGN:
Method |
Pretrained |
Rank@1 |
mAP |
mINP |
download |
SBS(R50-ibn) |
ImageNet |
85.1% |
65.4% |
18.4% |
- |
VeRi Baseline
SBS:
Method |
Pretrained |
Rank@1 |
mAP |
mINP |
download |
SBS(R50-ibn) |
ImageNet |
97.0% |
81.9% |
46.3% |
model |
VehicleID Baseline
BoT:
Test protocol: 10-fold cross-validation; trained on 4 NVIDIA P40 GPU.
Method |
Pretrained |
Testset size |
download |
Small |
Medium |
Large |
Rank@1 |
Rank@5 |
Rank@1 |
Rank@5 |
Rank@1 |
Rank@5 |
BoT(R50-ibn) |
ImageNet |
86.6% |
97.9% |
82.9% |
96.0% |
80.6% |
93.9% |
model |
VERI-Wild Baseline
BoT:
Test protocol: Trained on 4 NVIDIA P40 GPU.
Method |
Pretrained |
Testset size |
download |
Small |
Medium |
Large |
Rank@1 |
mAP |
mINP |
Rank@1 |
mAP |
mINP |
Rank@1 |
mAP |
mINP |
BoT(R50-ibn) |
ImageNet |
96.4% |
87.7% |
69.2% |
95.1% |
83.5% |
61.2% |
92.5% |
77.3% |
49.8% |
model |