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README.md

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+---
+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.