集成
将 Potato 与 AI 模型、众包平台连接，并导出到您常用的机器学习框架。
AI 与大语言模型集成
用 AI 辅助增强标注效率
Local LLMs (Ollama)
Run AI-assisted annotation with local LLMs using Ollama. Keep your data completely private.
查看文档 →HuggingFace
Access open-source models via HuggingFace Inference API for flexible AI assistance.
查看文档 →AI 驱动功能
- 智能标签建议
- 自动关键词高亮
- 质量检查辅助
- 预标注审核
- 解释生成
- 一致性检查
标注团队方案
使用您自己的团队或通过众包扩展规模
您自己的团队
推荐用于敏感数据在本地或您自己的服务器上运行 Potato，使用内部标注员。非常适合不能外泄的敏感数据、通过 IRB 审批的研究，或已有训练有素的标注团队的情况。
优势
或通过众包平台扩展规模
Prolific
Academic-friendly crowdsourcing with quality participants. Full integration with completion URLs and participant tracking.
功能特性
Amazon MTurk
Scale to thousands of annotators with Mechanical Turk integration. Supports qualifications and approval workflows.
功能特性
支持的数据格式
以任何常见格式导入数据
Text
.txt, .json, .jsonl
Images
.jpg, .png, .gif, .webp
Audio
.mp3, .wav, .ogg, .m4a
Video
.mp4, .webm, .mov
Documents
.pdf, .html
导出格式
将标注导出为主流机器学习格式
General
- JSON
Native Potato format with full annotation data
- JSONL
Line-delimited JSON for streaming and large datasets
- CSV
Tabular export for spreadsheet analysis
NLP
- CoNLL
Standard format for NER and sequence labeling
- Hugging Face
Direct export to HF Datasets format
- spaCy
Training data format for spaCy models
Computer Vision
- COCO
MS COCO format for object detection
- YOLO
YOLO format for real-time detection
- Pascal VOC
XML format for image classification
Python API 与命令行工具
用于自动化的编程接口
命令行
# Start annotation server potato start config.yaml # Export annotations potato export --format coco # Validate configuration potato validate config.yaml
Python API
from potato import Potato
# Load project
project = Potato("config.yaml")
# Get annotations
annotations = project.get_annotations()
# Export to DataFrame
df = project.to_dataframe()