| --- |
| license: agpl-3.0 |
| pipeline_tag: object-detection |
| tags: |
| - yolo |
| - finger |
| - detection |
| --- |
| |
| # CountHallu — RealHand Counting Model |
|
|
| Finger detector from **[Counting Hallucinations in Diffusion Models](https://arxiv.org/abs/2510.13080)** |
| (arXiv:2510.13080). A YOLO-based detector is used to count fingers in generated/real hand images, enabling the evaluation to flag hands with an incorrect number of fingers as counting hallucinations. |
|
|
| ## Architecture & checkpoint |
|
|
| - An **Ultralytics YOLO-v13** object detector, ships a single `model.pt`. |
| - One class, **`0 = finger`**; a correct hand has exactly **5** detected fingers. |
| - Inference settings used in the paper: `imgsz=640, conf=0.25, iou=0.1`. |
|
|
| > **Two-stage pipeline.** RealHand scoring first passes each image through the |
| > quality classifier |
| > [`CountHallu-quality_cls_model-RealHand`](https://huggingface.co/ShyFoo/CountHallu-quality_cls_model-RealHand), |
| > which filters out non-countable (visually failed) images; only clean images reach |
| > this finger detector. You need both models to calculate the counting hallucination rates in the RealHand dataset. |
|
|
| ## Usage |
|
|
| See the [CountHallu repository](<https://github.com/ShyFoo/CountHallu-Diff>) for the full evaluation protocol. |
|
|
| ```python |
| from ultralytics import YOLO |
| from huggingface_hub import hf_hub_download |
| |
| ckpt = hf_hub_download("ShyFoo/CountHallu-counting_model-RealHand", "model.pt") |
| model = YOLO(ckpt, task="detect") |
| results = model.predict(source="hand.png", imgsz=640, conf=0.25, iou=0.1) |
| num_fingers = len(results[0].boxes) |
| ``` |
|
|
| Or let the evaluation protocol fetch it for you: |
|
|
| ```python |
| from counthallu.utils import load_counting_model |
| model, model_type, ref_counts, target_classes = load_counting_model( |
| "realhand", use_hub_model=True, |
| repo_id="ShyFoo/CountHallu-counting_model-RealHand" |
| ) |
| ``` |
|
|
| ## License |
|
|
| This detector is trained with **Ultralytics YOLO-v13** on the [CountHallu-RealHand dataset](https://huggingface.co/datasets/ShyFoo/CountHallu-dataset-RealHand), which is **AGPL-3.0**. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{fu2025counting, |
| title={Counting Hallucinations in Diffusion Models}, |
| author={Fu, Shuai and Zhou, Jian and Chen, Qi and Jing, Huang and Nguyen, Huy Anh and Liu, Xiaohan and Zeng, Zhixiong and Ma, Lin and Zhang, Quanshi and Wu, Qi}, |
| journal={arXiv preprint arXiv:2510.13080}, |
| year={2025} |
| } |
| ``` |