KeenForgeAI/raccoon-corrected

Raccoon Detection Dataset — Corrected & Verified


A fully human-reviewed, re-annotated version of the classic Raccoon object detection dataset. Every one of the 193 images was manually reviewed; loose, missing, and incorrect bounding boxes were corrected.

中文摘要见文末 中文版修正报告。

Dataset at a glance

What was corrected (vs. the original annotations)

Every image was compared with the original VOC annotations using greedy IoU matching (matched ≥ 0.5, unchanged ≥ 0.95):

Key observations

• —The original boxes were often loose (e.g., raccoon-1: box (81,88)-(522,408) → tightened to (80,105)-(529,405), IoU 0.91). The average IoU of matched boxes is 0.810, i.e. the corrected boxes bound the raccoon noticeably more tightly.
• —Occluded / partially visible raccoons were kept with tight boxes; several missed instances were added.
• —7 exact/near duplicates were identified and removed.

Files

.
├── images/            # 193 images (jpg), unchanged from the original dataset
├── labels/            # 193 YOLO labels (one .txt per image)
├── splits/
│   ├── train.txt      # 153 images (same split as the original dataset)
│   └── test.txt       # 40 images
├── data.yaml          # Ultralytics dataset config
└── LICENSE

Label format

Standard YOLO: one line per object, normalized to [0, 1]:

class_id  x_center  y_center  width  height

class_id = 0 → raccoon

Quick start (Ultralytics)

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.train(data="data.yaml", epochs=100, imgsz=640)

metrics = model.val()   # evaluate on splits/test.txt

Source & license

• —Original dataset: experiencor/raccoon_dataset (fork of datitran/raccoon_dataset) — MIT License.
• —This corrected version: images unchanged; annotations fully re-drawn and verified. Released under the same MIT License, with attribution to the original authors.
• —If you use this dataset, please also credit the original Raccoon dataset.

How to cite

1. The original dataset — experiencor/raccoon_dataset (MIT License):

@misc{raccoon_dataset,
  author = {experiencor},
  title  = {Raccoon Dataset},
  year   = {2017},
  url    = {https://github.com/experiencor/raccoon_dataset}
}

2. This corrected release — the annotations were fully re-drawn and verified, so a citation to the original alone does not describe the data used here:

@misc{raccoon_corrected,
  author    = {KeenForgeAI},
  title     = {raccoon-corrected: a fully re-annotated release of the Raccoon detection dataset},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  doi       = {10.57967/hf/10528},
url       = {https://huggingface.co/datasets/KeenForgeAI/raccoon-corrected},
  note      = {Curated by Lu Gan and Sam Li. Original dataset: experiencor/raccoon_dataset (MIT).}
}

3. The annotation tool (optional):

@software{keenforge,
  author    = {KeenForgeAI},
  title     = {KeenForge: a local-first, offline image annotation and model-training desktop tool},
  year      = {2026},
  publisher = {KeenForgeAI},
  url       = {https://github.com/KeenForgeAI/KeenForge},
  note      = {MIT licensed. Developed by Lu Gan and Sam Li.}
}

How this version was made

• —Tool: KeenForge — an open-source, local-first auto-labeling and training desktop tool (YOLO training loop with human-in-the-loop review).
• —Process: every image opened and reviewed manually; each raccoon re-boxed tightly; missed animals added; non-raccoon objects and duplicates removed.
• —Comparison statistics computed by greedy IoU matching between the original VOC boxes and the corrected YOLO boxes.

中文版修正报告

这是一个经过完整人工复核、重新标注的经典 Raccoon 检测数据集修正版。

修正内容（与原始标注对比，贪心 IoU 匹配：≥0.5 视为同一目标，≥0.95 视为未修改）：

• —框收紧/调整：182 个（平均 IoU 0.810 —— 原始框普遍偏松，修正后紧贴目标）
• —未修改：22 个
• —删除错误框：6 个
• —补充漏标：7 个（原标注遗漏的浣熊）
• —移除重复图片：7 张（raccoon-45/50/74/83/85/98/116）

标注工具：KeenForge（本地化标注 + 训练闭环工具）

许可：原始数据集为 MIT 许可；本修正版沿用 MIT，图片未改动，标注全部人工复核重画，请同时注明原始数据集来源。