Releases: Geekgineer/motcpp
Releases · Geekgineer/motcpp
reid-models-v1.0.0
ReID Weight Files (.pt) for Appearance-Based Trackers
A collection of 33 ReID models adapted for appearance-feature-based multi-object trackers.
Adapted from:
These models are typically used in StrongSORT, DeepSORT variants, and other ReID-based MOT pipelines.
Model Families Overview
| Model Family | # Models | Description |
|---|---|---|
| ResNet50 | 6 | Strong baseline backbone |
| MLFN | 3 | Multi-Level Factorization Network |
| HACNN | 3 | Attention-based ReID model |
| MobileNetV2 | 6 | Lightweight, real-time friendly |
| OSNet | 15 | Omni-scale, state-of-the-art |
Total models: 33
Model Family Details
ResNet50 (6)
- Classic CNN-based ReID backbone
- Good balance between accuracy and speed
- Common baseline for person ReID tasks
MLFN – Multi-Level Factorization Net (3)
- Learns discriminative latent factors
- Robust to pose, viewpoint, and appearance changes
HACNN – Harmonious Attention CNN (3)
- Combines spatial and channel attention
- Focused on fine-grained person discrimination
MobileNetV2 (6)
- Optimized for low latency and efficiency
- Suitable for real-time tracking and edge devices
- Lower computational cost
OSNet – Omni-Scale Network (15)
Available variants:
- OSNet x1.0
- OSNet x0.75
- OSNet x0.5
- OSNet x0.25
- OSNet-IBN (Instance-Batch Normalization)
- OSNet-AIN (Adaptive Instance Normalization)
Key advantages:
- Excellent generalization across datasets
- Strong performance in multi-object tracking
- Scalable architectures for speed/accuracy trade-offs
Typical Applications
- Appearance feature extraction for MOT
- StrongSORT / DeepSORT-based trackers
- Person re-identification across cameras
- Offline and real-time tracking pipelines
**
benchmark-data-v1.0
✓ MOT17-mini.tar.gz (2.8M) - Test sequences
✓ yolox_dets.tar.gz (113M) - YOLOX detections
✓ reid_embs.tar.gz (110M) - ReID embeddings
These data are used for the CI Pipeline.