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End of preview. Expand in Data Studio
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
YOLO-DeskDataset
🧾 Dataset Summary
YOLO-DeskDataset 是一个用于检测桌面物品的图像数据集，旨在帮助训练目标检测模型（如 YOLOv8）识别桌面上常见的物品。
数据集包括原图约 500 张，经过数据增强扩充至 3000+ 张。
📊 Dataset Structure
- 图片数量：约 3000+ 张（含数据增强）
- 类别：共 80 类桌面物品，详见下表
- 标注格式：YOLO txt（每张图片对应一个 txt 文件，记录物体类别和边界框坐标）
- Split：训练集 / 验证集，按 80% / 20% 划分
🏷 Classes
| Class ID | Class Name |
|---|---|
| 0 | pen |
| 1 | eraser |
| 2 | ruler |
| 3 | scissors |
| 4 | sticky notes |
| 5 | paper clip |
| 6 | stapler |
| 7 | computer |
| 8 | laptop |
| 9 | mouse |
| 10 | keyboard |
| 11 | monitor |
| 12 | headphones |
| 13 | earphones |
| 14 | mobile phone |
| 15 | tablet |
| 16 | charger |
| 17 | cable |
| 18 | book |
| 19 | document |
| 20 | cup |
| 21 | thermos cup |
| 22 | plastic cup |
| 23 | keys |
| 24 | wallet |
| 25 | glasses |
| 26 | watch |
| 27 | mouse pad |
| 28 | package |
| 29 | snack box |
| 30 | carton |
| 31 | box |
| 32 | plastic bottle |
| 33 | can |
| 34 | disposable cup |
| 35 | bottle cap |
| 36 | straw |
| 37 | snack leftovers |
| 38 | food wrapper |
| 39 | paper |
| 40 | waste paperball |
| 41 | waste package |
| 42 | tissue |
| 43 | towel |
| 44 | plastic bag |
| 45 | broken items |
| 46 | debris |
| 47 | chopsticks |
| 48 | food container |
| 49 | instant noodles |
| 50 | toy |
| 51 | takeout |
| 52 | fan |
| 53 | bowl |
| 54 | mask |
| 55 | waterpot |
| 56 | lamp |
| 57 | card |
| 58 | backpack |
| 59 | handbag |
| 60 | cap |
| 61 | pencil case |
| 62 | umbrella |
| 63 | comb |
| 64 | power bank |
| 65 | mirror |
| 66 | socket |
| 67 | phone holder |
| 68 | calculator |
| 69 | clock |
| 70 | apple |
| 71 | printer |
| 72 | bottle |
| 73 | ointment |
| 74 | banana |
| 75 | orange |
| 76 | speaker |
| 77 | remote controller |
| 78 | gamepad |
| 79 | squeeze bottle |
📸 Data Collection
- 来源：主要来自社交平台图片，小部分为自己拍摄
- 采集地区：多种环境的桌面场景
- 隐私：不包含人物正面照或敏感信息
- 数据清洗：对图片和标注进行了筛选与整理
🏷 Annotation
- 格式：YOLO txt
- 工具：LabelImg 等标注工具
- 标注方式：人工标注 + 数据增强
- 复核：标注后进行了基础检查，保证边界框与标签对应
🧠 Use Cases
- 训练目标检测模型（YOLO 系列）
- 桌面整理多模态 agent 的物品识别模块
- 计算机视觉研究中小型桌面场景的目标检测任务
⚠ Limitations and Bias
- 数据集主要来源于社交平台，桌面场景可能偏向干净整齐
- 部分类别样本量较少
- 光照、角度和桌面风格多样性有限
- 可能对复杂或非常混乱的桌面场景泛化能力不足
📜 License
- CC BY 4.0
🛠 How to Use
from datasets import load_dataset
# 加载数据集
dataset = load_dataset("hhh134/YOLO-DeskDataset")
train_data = dataset['train']
valid_data = dataset['validation']
# 查看每条数据
for item in train_data:
print(item['image'], item['labels'])
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