이미지 주석
경계 상자, 다각형, 분류 레이블로 Potato에서 이미지 주석을 설정합니다. 확대/축소, 패닝, 다중 레이블, AI 지원 사전 레이블링을 지원합니다.
Potato는 분류, 객체 탐지, 영역 레이블링 작업을 위한 이미지 주석을 지원합니다.
이미지 표시 활성화
yaml
image:
enabled: true
max_width: 800
max_height: 600데이터 형식
데이터에서 이미지를 참조합니다.
json
{
"id": "img_1",
"image_path": "images/photo_001.jpg",
"description": "Optional description"
}이미지 필드를 설정합니다.
yaml
data_files:
- path: data/image_tasks.json
image_field: image_path이미지 분류
이미지 전체를 분류합니다.
yaml
annotation_schemes:
- annotation_type: radio
name: category
description: "What is shown in this image?"
labels:
- Cat
- Dog
- Bird
- Other
- annotation_type: multiselect
name: attributes
description: "Select all that apply"
labels:
- Indoor
- Outdoor
- Multiple animals
- Human present다중 레이블 분류
yaml
annotation_schemes:
- annotation_type: multiselect
name: objects
description: "What objects are visible?"
labels:
- Person
- Car
- Building
- Tree
- Animal
- Furniture
- Food
- Electronic device이미지 품질 평가
yaml
annotation_schemes:
- annotation_type: likert
name: quality
description: "Overall image quality"
size: 5
min_label: "Very poor"
max_label: "Excellent"
- annotation_type: multiselect
name: issues
description: "Select any quality issues"
labels:
- Blurry
- Overexposed
- Underexposed
- Noisy
- Low resolution
- Watermark visible경계 상자 주석
객체 주위에 상자를 그립니다.
yaml
annotation_schemes:
- annotation_type: bbox
name: objects
description: "Draw boxes around objects"
labels:
- Person
- Car
- Bicycle
- Traffic sign
label_colors:
Person: "#3b82f6"
Car: "#10b981"
Bicycle: "#f59e0b"
"Traffic sign": "#ef4444"경계 상자 출력
json
{
"id": "img_1",
"objects": [
{
"label": "Person",
"x": 100,
"y": 50,
"width": 80,
"height": 200
},
{
"label": "Car",
"x": 300,
"y": 150,
"width": 150,
"height": 100
}
]
}미리 로드된 경계 상자
검토를 위해 기존 주석을 불러옵니다.
json
{
"id": "img_1",
"image_path": "images/photo_001.jpg",
"predictions": [
{"label": "Person", "x": 100, "y": 50, "width": 80, "height": 200, "confidence": 0.95}
]
}yaml
annotation_schemes:
- annotation_type: bbox
name: objects
load_predictions: true
prediction_field: predictions영역/다각형 주석
직사각형이 아닌 영역의 경우:
yaml
annotation_schemes:
- annotation_type: polygon
name: regions
description: "Outline regions of interest"
labels:
- Building
- Road
- Vegetation
- Water이미지 비교
두 이미지를 비교합니다.
yaml
data_files:
- path: data/image_pairs.json
item_a_field: image_original
item_b_field: image_edited
annotation_schemes:
- annotation_type: pairwise
name: preference
description: "Which image looks better?"
options:
- label: "Original"
value: "A"
- label: "Edited"
value: "B"
- label: "Same"
value: "tie"이미지 캡셔닝
yaml
annotation_schemes:
- annotation_type: text
name: caption
description: "Write a caption for this image"
textarea: true
placeholder: "Describe what you see..."
min_length: 10
max_length: 300캡션 품질 검토
yaml
data_files:
- path: data/captions.json
image_field: image_path
text_field: generated_caption
annotation_schemes:
- annotation_type: likert
name: accuracy
description: "How accurate is this caption?"
size: 5
min_label: "Very inaccurate"
max_label: "Very accurate"
- annotation_type: likert
name: fluency
description: "How natural is the language?"
size: 5
min_label: "Very awkward"
max_label: "Very natural"
- annotation_type: text
name: improved_caption
description: "Suggest a better caption (optional)"
textarea: true표시 옵션
이미지 크기 조정
yaml
image:
max_width: 800
max_height: 600
preserve_aspect_ratio: true확대/축소 제어
yaml
image:
zoom_enabled: true
initial_zoom: fit # 'fit', 'actual', or percentage전체 화면 모드
yaml
image:
fullscreen_enabled: true콘텐츠 검수
yaml
annotation_schemes:
- annotation_type: radio
name: safe_for_work
description: "Is this image safe for work?"
labels:
- Safe
- Questionable
- Not Safe
- annotation_type: multiselect
name: violation_types
description: "Select all violations (if any)"
labels:
- Violence
- Adult content
- Hate symbols
- Graphic content
- Spam/advertisement
show_if:
scheme: safe_for_work
value: ["Questionable", "Not Safe"]지원되는 형식
지원되는 일반적인 이미지 형식:
- JPEG/JPG
- PNG
- GIF
- WebP
- BMP
yaml
image:
allowed_formats: ["jpg", "jpeg", "png", "webp"]전체 예제: 객체 탐지 검토
yaml
task_name: "Object Detection Verification"
image:
enabled: true
max_width: 1000
zoom_enabled: true
data_files:
- path: data/detections.json
image_field: image_path
annotation_schemes:
# Review pre-loaded predictions
- annotation_type: bbox
name: objects
description: "Verify and correct object boxes"
labels:
- Person
- Vehicle
- Animal
- Object
load_predictions: true
prediction_field: model_predictions
label_colors:
Person: "#3b82f6"
Vehicle: "#10b981"
Animal: "#f59e0b"
Object: "#6b7280"
# Overall assessment
- annotation_type: radio
name: prediction_quality
description: "How accurate were the predictions?"
labels:
- All correct
- Minor corrections needed
- Major corrections needed
- Mostly incorrect
- annotation_type: number
name: missed_objects
description: "How many objects were missed?"
min: 0
max: 50
- annotation_type: text
name: notes
description: "Any issues or comments?"
textarea: true
required: false성능 팁
- 이미지 크기 최적화 - 주석 작업 전에 큰 이미지의 크기를 조정합니다
- 사진에는 JPEG 사용 - 파일 크기가 작고 로딩이 빠릅니다
- 그래픽에는 PNG 사용 - 다이어그램/스크린샷에 더 나은 품질을 제공합니다
- 지연 로딩 활성화 - 대용량 데이터셋에 사용합니다
- 썸네일 고려 - 목록 보기에서 미리보기를 표시합니다
- 일관된 전처리 - 크기와 형식을 정규화합니다