blank4hd/mctrack-reid

MCTrack Re-ID Model

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)

Variant	IDF1	IDP	IDR
60-epoch	16.99	23.80	13.21
120-epoch	18.72	26.41	14.50

Usage

Download and load with huggingface_hub:

from huggingface_hub import hf_hub_download
import torch

ckpt_path = hf_hub_download(
    repo_id="blank4hd/mctrack-reid",
    filename="best_60ep.pth",
)

state = torch.load(ckpt_path, map_location="cpu", weights_only=False)
model_state = state["state_dict"]

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)