Person re-identification model trained on Market-1501 as part of the
MSML 640 (Computer Vision) Final Project at the University of Maryland.
This model is the appearance backbone used in our multi-camera object tracking
system.
Model Variants
This repo contains two checkpoints:
best_60ep.pth (primary) - trained for 60 epochs.
Used as the deployed Re-ID model in our final cross-camera demos due to
qualitatively cleaner cluster outputs.
best_120ep.pth (ablation) - same recipe, trained for 120 epochs.
Slightly higher Re-ID accuracy but only marginal improvement on downstream
tasks (see "Performance" below).
Both checkpoints contain only the model state_dict. No optimizer or scheduler
state.
Architecture
Backbone: ResNet-50 (ImageNet-initialized; final stride changed from
2 to 1 to retain higher-resolution features)
Pooling: Global average pooling
Neck: BNNeck (Batch Normalization neck) - separates triplet-loss
features from classification features
Embedding dimension: 256
Total parameters: ~25M
Training Recipe
Setting
Value
Dataset
Market-1501 (12,936 train images, 751 train IDs)
Identity sampler
P=16 IDs x K=4 instances per batch
Batch size
64
Optimizer
Adam, lr=3.5e-4, weight_decay=5e-4
LR schedule
Linear warmup (10 epochs), step decay (x0.1 at epochs 40 and 70)
Loss
Combined CE (label smoothing 0.1) + triplet (soft margin)
Image size
256x128
Augmentation
Random horizontal flip, random erasing
Performance
Standalone Re-ID (Market-1501)
Variant
mAP
Rank-1
Rank-5
Rank-10
60-epoch
73.73
89.32
96.04
97.74
120-epoch
75.15
90.66
96.79
98.04
Downstream - Single-camera tracking (MOT17, with DeepSORT)
Variant
HOTA
MOTA
IDF1
IDSW
60-epoch
41.1
36.5
48.6
260
120-epoch
41.0
36.6
48.8
246
Downstream - Cross-camera tracking (Wildtrack, with ground-plane filter)
To use this with the model architecture, you need the ReIDModel class from
the project repository. A minimal standalone loader (load_reid.py) is
provided alongside this model card with the architecture definition inlined,
so the model can be used without cloning the full project.
Intended Use
This model produces 256-dimensional appearance embeddings for cropped person
images. Two crops of the same person are expected to produce embeddings with
high cosine similarity; crops of different people produce embeddings with low
similarity.
Suitable for:
Person re-identification in research / academic settings
Appearance feature extraction in tracking pipelines (e.g., DeepSORT)
Educational demonstration of metric learning
Not suitable for:
Surveillance applications without explicit consent
Identification of individuals across populations (high false-positive rate
in cross-domain settings)
Any use where reliability is safety-critical
Limitations
Domain gap. Trained on Market-1501 (Tsinghua University campus, ~5
surveillance cameras). Performance degrades on outdoor pedestrian-square
scenes (e.g., Wildtrack), where IDF1 drops to ~17-19% in cross-camera
matching.
Person crops only. Expects the input to be a tightly-cropped person
image. Whole scenes or non-person inputs produce meaningless embeddings.
Resolution sensitive. Trained at 256x128. Significantly different input
resolutions will degrade quality.
No fairness audit. Not evaluated for performance disparities across
demographic groups.
Training Details (compute and time)
Hardware: Apple M4 Pro (MPS backend)
Per-epoch time: ~46 seconds
Total training time: 60-epoch ~46 min; 120-epoch ~92 min
Memory usage: ~3 GB unified memory at batch size 64
Citation
This work was completed for the MSML 640 final project, Spring 2026.
Group 9 - MSML 640 Final Project
Multi-Camera Object Tracking with Person Re-Identification
Acknowledgments
Architecture inspired by Luo et al. ("Bag of Tricks and a Strong Baseline
for Deep Person Re-Identification", CVPR Workshop 2019)
BNNeck design from the same paper
Triplet loss formulation from Hermans et al. ("In Defense of the Triplet
Loss for Person Re-Identification", arXiv 2017)
Market-1501 dataset from Zheng et al. ("Scalable Person Re-Identification:
A Benchmark", ICCV 2015)
