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資料格式定義 - YOLO segmentation 格式：
用於訓練 YOLO 分割模型的資料集標籤格式如下：
<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
- 每個圖像對應一個文字檔案：資料集中的每個圖像都有一個對應的文字文件，該文字檔案與圖像檔案同名，擴展名為 ".txt"。
- 每行一個物件：文字檔案中的每一行對應於圖像中的一個物件實例。
- 每行包含的物件資訊：每行包含關於物件實例的以下資訊：
- 物件類別索引：一個整數，表示物件的類別（例如，0 代表人，1 代表汽車等）。
- 物件邊界座標：圍繞遮罩區域的邊界座標，已歸一化到 0 和 1 之間。
- 注意：不是 bbox; 而是多邊形（Polygon) 座標。
<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
範例
在這種格式中， <class-index> 是物件類別的索引，以及 <x1> <y1> <x2> <y2> ... <xn> <yn> 是物件segmentation遮罩的歸一化多邊形座標（值在 [0, 1] 相對於影像寬度和高度）。座標之間用空格分隔。
以下是一個YOLO資料集格式的範例，用於包含兩個物件的單張影像，其中一個物件由3點segment組成，另一個由5點segment組成。
0 0.681 0.485 0.670 0.487 0.676 0.4871 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
每行的長度不必相等。每個分割標籤必須有一個 最少 3 個 (x, y) 點: <class-index> <x1> <y1> <x2> <y2> <x3> <y3>
以下是一個YOLO資料集格式的範例，用於包含兩個物件的單張影像，其中一個物件由3點segment組成，另一個由5點segment組成。
0 0.681 0.485 0.670 0.487 0.676 0.4871 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
每行的長度不必相等。每個分割標籤必須有一個 最少 3 個 (x, y) 點: <class-index> <x1> <y1> <x2> <y2> <x3> <y3>
資料集 YAML 格式
Ultralytics 框架使用 YAML 檔案格式來定義訓練分割模型的資料集和模型配置。
以下是用於定義分割資料集的 YAML 格式範例：
dataset/├── images/│ ├── train/│ ├── val/│├── labels/│ ├── train/│ ├── val/│└── dataset.yaml
dataset.yaml
path: dataset
train: images/trainval: images/val
names: 0: scratch 1: hole
train: images/trainval: images/val
names: 0: scratch 1: hole
官方範例
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/# Example usage: yolo train data=coco8-seg.yaml# parent# ├── ultralytics# └── datasets# └── coco8-seg ← downloads here (1 MB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]path: coco8-seg # dataset root dirtrain: images/train # train images (relative to 'path') 4 imagesval: images/val # val images (relative to 'path') 4 imagestest: # test images (optional)
# Classesnames: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: boat 9: traffic light 10: fire hydrant 11: stop sign 12: parking meter 13: bench 14: bird 15: cat 16: dog 17: horse 18: sheep 19: cow 20: elephant 21: bear 22: zebra 23: giraffe 24: backpack 25: umbrella 26: handbag 27: tie 28: suitcase 29: frisbee 30: skis 31: snowboard 32: sports ball 33: kite 34: baseball bat 35: baseball glove 36: skateboard 37: surfboard 38: tennis racket 39: bottle 40: wine glass 41: cup 42: fork 43: knife 44: spoon 45: bowl 46: banana 47: apple 48: sandwich 49: orange 50: broccoli 51: carrot 52: hot dog 53: pizza 54: donut 55: cake 56: chair 57: couch 58: potted plant 59: bed 60: dining table 61: toilet 62: tv 63: laptop 64: mouse 65: remote 66: keyboard 67: cell phone 68: microwave 69: oven 70: toaster 71: sink 72: refrigerator 73: book 74: clock 75: vase 76: scissors 77: teddy bear 78: hair drier 79: toothbrush
# Download script/URL (optional)download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip
# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/# Example usage: yolo train data=coco8-seg.yaml# parent# ├── ultralytics# └── datasets# └── coco8-seg ← downloads here (1 MB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]path: coco8-seg # dataset root dirtrain: images/train # train images (relative to 'path') 4 imagesval: images/val # val images (relative to 'path') 4 imagestest: # test images (optional)
# Classesnames: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane 5: bus 6: train 7: truck 8: boat 9: traffic light 10: fire hydrant 11: stop sign 12: parking meter 13: bench 14: bird 15: cat 16: dog 17: horse 18: sheep 19: cow 20: elephant 21: bear 22: zebra 23: giraffe 24: backpack 25: umbrella 26: handbag 27: tie 28: suitcase 29: frisbee 30: skis 31: snowboard 32: sports ball 33: kite 34: baseball bat 35: baseball glove 36: skateboard 37: surfboard 38: tennis racket 39: bottle 40: wine glass 41: cup 42: fork 43: knife 44: spoon 45: bowl 46: banana 47: apple 48: sandwich 49: orange 50: broccoli 51: carrot 52: hot dog 53: pizza 54: donut 55: cake 56: chair 57: couch 58: potted plant 59: bed 60: dining table 61: toilet 62: tv 63: laptop 64: mouse 65: remote 66: keyboard 67: cell phone 68: microwave 69: oven 70: toaster 71: sink 72: refrigerator 73: book 74: clock 75: vase 76: scissors 77: teddy bear 78: hair drier 79: toothbrush
# Download script/URL (optional)download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip
多個來源目錄時
使用「列表（list）」指定多個資料夾
path: /data # 可選，作為相對路徑基準
train: - train1/images - train2/images - /another/path/train3/images
val: - val1/images - val2/images
test: - test1/images
names: 0: person 1: car 2: dog
train: - train1/images - train2/images - /another/path/train3/images
val: - val1/images - val2/images
test: - test1/images
names: 0: person 1: car 2: dog
訓練用法
# Start training from a pretrained *.pt modelyolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
WBC dataset
目標預測結果
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