File size: 4,164 Bytes
b314e90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | ---
license: mit
pipeline_tag: object-detection
library_name: ultralytics
tags:
- object-detection
- yolo
- rt-detr
- autonomous-driving
- long-tail-classification
- bangladesh
---
# CHARD: Characteristic-Aware Hierarchical Detection
Trained checkpoints for **"CHARD: Characteristic-Aware Hierarchical Detection for Bangladesh Road Scenes"**. Code: [github.com/euler1729/chard](https://github.com/euler1729/chard).
CHARD turns the physical characteristics of a vehicle (wheel count, size, propulsion mechanism) into auxiliary supervision, letting rare, safety-critical vehicle classes — auto-rickshaws, cart vehicles, wheelchairs — borrow statistical strength from the more common classes that share their attributes. It's implemented in two forms: an end-to-end hierarchical attribute-then-class head on RT-DETR, and a lightweight auxiliary attribute branch on YOLO that's discarded before inference (zero deployment cost).
This repository holds the **79 checkpoints** behind the paper's results: the primary single-run baselines plus the full 72-run multi-seed × multi-dataset study (8 model variants × 3 seeds × 3 Bangladeshi road-vehicle datasets — BadODD, Poribohon-BD, Sorokh-Poth).
## Headline result (BadODD, test split, mean ± std over 3 seeds)
| Model | mAP@[.5:.95] | Tail-class AP |
|---|---|---|
| YOLOv8-l | 42.6% | 28.7% |
| YOLOv10-l | 39.4% | 23.0% |
| YOLOv11-l | 42.5% | 28.0% |
| RT-DETR-l | 41.1% | 29.7% |
| **CHARD-YOLOv11 (attr)** | **44.3%** | **31.9%** |
CHARD-YOLOv11 (attr) improves on its matched no-attribute control by +2.4 points (paired *t*-test, *p* = 0.003) at identical inference latency to the vanilla backbone. Full results: [`benchmarks/`](https://github.com/euler1729/chard/tree/main/benchmarks) in the code repo.
## Files
```
badodd_seed0/ # primary single-run baselines + CHARD (seed 0), Table 5/6 of the paper
yolov8l.pt yolov10l.pt yolo11l.pt rtdetr_l.pt
chard_rtdetr.pt chard_yolov8l.pt chard_yolov8l_attr.pt
badodd/ # 72-run multi-seed study, Table 9 — 24 files per dataset dir:
poribohon_bd/ # {yolov8l,yolov10l,yolo11l,rtdetr_l}_s{0,1,2}.pt
vehicle_data/ # chard_{yolov8l,yolo11l}_{attr,noattr}_s{0,1,2}.pt
# (vehicle_data = "Sorokh-Poth" in the paper)
```
All checkpoints are stripped of optimizer/scheduler/EMA state and stored in FP16 (see [`prepare_release_weights.py`](https://github.com/euler1729/chard/blob/main/prepare_release_weights.py)).
## Loading
**Plain YOLO baselines** (`yolov8l`, `yolov10l`, `yolo11l`) load directly with Ultralytics:
```python
from ultralytics import YOLO
model = YOLO("badodd_seed0/yolo11l.pt")
results = model.predict("image.jpg")
```
**RT-DETR baseline** (`rtdetr_l`) likewise:
```python
from ultralytics import RTDETR
model = RTDETR("badodd_seed0/rtdetr_l.pt")
```
**CHARD-YOLO checkpoints** (`chard_yolov8l*`, `chard_yolo11l*`) are Ultralytics `DetectionModel` subclasses with an added attribute head — you need the `chard_yolo` package from the code repo registered before unpickling:
```python
from ultralytics import YOLO
from chard_yolo.model import AttrDetectionModel # noqa: F401 — required for unpickling
model = YOLO("badodd/chard_yolo11l_attr_s0.pt")
```
**CHARD-RT-DETR** (`chard_rtdetr.pt`) is a raw `state_dict` from a custom training loop (not an Ultralytics/Transformers checkpoint) — reconstruct the architecture from `chard_model.py` in the code repo, then:
```python
import torch
ckpt = torch.load("badodd_seed0/chard_rtdetr.pt", map_location="cpu")
model.load_state_dict(ckpt["model"])
```
## Citation
```bibtex
@article{hasan2026chard,
title = {{CHARD}: Characteristic-Aware Hierarchical Detection for {Bangladesh} Road Scenes},
author = {Hasan, Mahmudul and Fahad, Istiaq Ahmed and Arefin, Md Fahim and Khan, Md Mosaddek},
journal = {IEEE Access},
year = {2026},
note = {In press}
}
```
## License
MIT — see [LICENSE](https://github.com/euler1729/chard/blob/main/LICENSE) in the code repo. Underlying dataset licenses (BadODD, Poribohon-BD, Sorokh-Poth) are governed by their original sources.
|