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FastReID Model Zoo and Baselines

Introduction

This file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA P40 GPU. The software in use were PyTorch 1.4, 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.
  • The model id column is provided for ease of reference. To check downloaded file integrity, any model on this page contains tis md5 prefix in its file name.
  • Training curves and other statistics can be found in metrics for each 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):

  1. Circle loss
  2. Freeze backbone training
  3. Cutout data augmentation & Auto Augmentation
  4. Cosine annealing learning rate decay
  5. Soft margin triplet loss

Bag of Specials(BoS):

  1. Non-local block
  2. GeM pooling

Market1501 Baselines

BoT:

Method Pretrained Rank@1 mAP mINP download
BoT(R50) ImageNet 94.4% 86.1% 59.4% -
BoT(R50-ibn) ImageNet 94.9% 87.6% 64.1% -
BoT(S50) ImageNet 95.1% 88.5% 66.0% -
BoT(R101-ibn) ImageNet 95.4% 88.9% 67.4% -

AGW:

Method Pretrained Rank@1 mAP mINP download
AGW(R50) ImageNet 95.3% 88.2% 66.3% -
AGW(R50-ibn) ImageNet 95.1% 88.7% 67.1% -
AGW(S50) ImageNet 94.7% 87.1% 62.2% -
AGW(R101-ibn) ImageNet 95.5% 89.5% 69.5% -

SBS:

Method Pretrained Rank@1 mAP mINP download
SBS(R50) ImageNet 95.4% 88.2% 64.8% -
SBS(R50-ibn) ImageNet 95.7% 89.3% 67.5% -
SBS(S50) ImageNet 95.0% 87.0% 60.6% -
SBS(R101-ibn) ImageNet 96.3% 90.3% 70.0% -

MGN:

Method Pretrained Rank@1 mAP mINP download
SBS(R50-ibn) ImageNet 95.8% 89.7% 67.0% -

DukeMTMC Baseline

BoT:

Method Pretrained Rank@1 mAP mINP download
BoT(R50) ImageNet 87.1% 76.9% 41.6% -
BoT(R50-ibn) ImageNet 89.6% 79.1% 44.4% -
BoT(S50) ImageNet 87.8% 77.7% 39.6% -
BoT(R101-ibn) ImageNet 91.1% 81.3% 47.7% -

AGW:

Method Pretrained Rank@1 mAP mINP download
AGW(R50) ImageNet 89.0% 79.9% 46.3% -
AGW(R50-ibn) ImageNet 89.8% 80.7% 47.7% -
AGW(S50) ImageNet 89.9% 79.7% 44.2% -
AGW(R101-ibn) ImageNet 91.4% 82.1% 50.2% -

SBS:

Method Pretrained Rank@1 mAP mINP download
SBS(R50) ImageNet 89.6% 79.8% 44.6% -
SBS(R50-ibn) ImageNet 91.3% 81.6% 47.6% -
SBS(S50) ImageNet 90.5% 79.1% 42.7% -
SBS(R101-ibn) ImageNet 92.4% 83.2% 49.7% -

MGN:

Method Pretrained Rank@1 mAP mINP download
SBS(R50-ibn) ImageNet 91.6% 82.1% 46.7% -

MSMT17 Baseline

BoT:

Method Pretrained Rank@1 mAP mINP download
BoT(R50) ImageNet 72.3% 48.3% 9.7% -
BoT(R50-ibn) ImageNet 77.0% 54.4% 12.5% -
BoT(S50) ImageNet 80.4% 59.2% 15.9% -
BoT(R101-ibn) ImageNet 79.0% 57.5% 14.6% -

AGW:

Method Pretrained Rank@1 mAP mINP download
AGW(R50) ImageNet 76.7% 53.6% 12.2% -
AGW(R50-ibn) ImageNet 79.3% 57.5% 14.3% -
AGW(S50) ImageNet 77.3% 54.7% 12.6% -
AGW(R101-ibn) ImageNet 80.8% 60.2% 16.5% -

SBS:

Method Pretrained Rank@1 mAP mINP download
SBS(R50) ImageNet 83.3% 59.9% 14.6% -
SBS(R50-ibn) ImageNet 84.0% 61.2% 15.5% -
SBS(S50) ImageNet 82.6% 58.2% 13.2% -
SBS(R101-ibn) ImageNet 85.1% 63.3% 16.6% -

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% -

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% -

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% -