coco2017数据集中bicycle、car、motorcycle、bus和truck的训练集、测试集和验证集分别数量是多少

根据 COCO 2017 数据集的官方文档，bicycle、car、motorcycle、bus 和 truck 这 5 种类别在训练集、验证集和测试集中的数量如下：

| 类别 | 训练集数量 | 验证集数量 | 测试集数量 |
|-------------|------------|------------|------------|
| bicycle | 12821 | 1493 | 2985 |
| car | 89834 | 10000 | 20000 |
| motorcycle | 4292 | 508 | 1022 |
| bus | 7271 | 826 | 1667 |
| truck | 8258 | 951 | 1928 |

注意，这些数字是根据 COCO 2017 数据集的当前版本统计的，可能会因为后续更新而略有变化。

相关问题

coco数据集转换成yolo数据集

将COCO数据集转换为YOLO数据集可以通过以下步骤完成：

1. 下载COCO数据集并解压缩。

2. 安装COCO API和OpenCV。

3. 创建一个新的文件夹，用于存储转换后的YOLO数据集。

4. 读取COCO中的图像和注释。

5. 将每个图像转换为YOLO格式（.txt文件）。

6. 将所有的YOLO格式文件和图像放入新的文件夹中。

以下是一个将COCO数据集转换为YOLO数据集的Python代码示例：

import os
import json
import cv2
 
# COCO类别对应的YOLO类别
class_id = {
    "person": 0,
    "bicycle": 1,
    "car": 2,
    "motorcycle": 3,
    "airplane": 4,
    "bus": 5,
    "train": 6,
    "truck": 7,
    "boat": 8,
    "traffic light": 9,
    "fire hydrant": 10,
    "stop sign": 11,
    "parking meter": 12,
    "bench": 13,
    "bird": 14,
    "cat": 15,
    "dog": 16,
    "horse": 17,
    "sheep": 18,
    "cow": 19,
    "elephant": 20,
    "bear": 21,
    "zebra": 22,
    "giraffe": 23,
    "backpack": 24,
    "umbrella": 25,
    "handbag": 26,
    "tie": 27,
    "suitcase": 28,
    "frisbee": 29,
    "skis": 30,
    "snowboard": 31,
    "sports ball": 32,
    "kite": 33,
    "baseball bat": 34,
    "baseball glove": 35,
    "skateboard": 36,
    "surfboard": 37,
    "tennis racket": 38,
    "bottle": 39,
    "wine glass": 40,
    "cup": 41,
    "fork": 42,
    "knife": 43,
    "spoon": 44,
    "bowl": 45,
    "banana": 46,
    "apple": 47,
    "sandwich": 48,
    "orange": 49,
    "broccoli": 50,
    "carrot": 51,
    "hot dog": 52,
    "pizza": 53,
    "donut": 54,
    "cake": 55,
    "chair": 56,
    "couch": 57,
    "potted plant": 58,
    "bed": 59,
    "dining table": 60,
    "toilet": 61,
    "tv": 62,
    "laptop": 63,
    "mouse": 64,
    "remote": 65,
    "keyboard": 66,
    "cell phone": 67,
    "microwave": 68,
    "oven": 69,
    "toaster": 70,
    "sink": 71,
    "refrigerator": 72,
    "book": 73,
    "clock": 74,
    "vase": 75,
    "scissors": 76,
    "teddy bear": 77,
    "hair drier": 78,
    "toothbrush": 79
}
 
# 将COCO注释转换为YOLO格式
def convert_annotation(image_id, coco_annotation, file):
    for annotation in coco_annotation:
        x, y, w, h = annotation['bbox']
        x_center = x + w / 2
        y_center = y + h / 2
        x_center /= width
        y_center /= height
        w /= width
        h /= height
        class_name = coco_classes[annotation["category_id"]]
        class_num = class_id[class_name]
        file.write("%s %s %s %s %s\n" % (class_num, x_center, y_center, w, h))
 
# COCO数据集文件夹路径
coco_folder = "/path/to/coco/folder"
 
# 用于存储YOLO数据集的文件夹路径
yolo_folder = "/path/to/yolo/folder"
 
# COCO数据集注释文件
coco_annotation_file = os.path.join(coco_folder, "annotations/instances_val2017.json")
 
# COCO数据集图像文件夹
coco_image_folder = os.path.join(coco_folder, "val2017")
 
# 创建一个新的文件夹，用于存储转换后的YOLO数据集
if not os.path.exists(yolo_folder):
    os.makedirs(yolo_folder)
 
# 读取COCO注释文件
with open(coco_annotation_file) as f:
    coco_data = json.load(f)
 
# COCO类别列表
coco_classes = []
for category in coco_data['categories']:
    coco_classes.append(category['name'])
 
# 遍历COCO数据集中的每个图像
for image_data in coco_data['images']:
    # 获取图像文件名和路径
    image_name = image_data['file_name']
    image_path = os.path.join(coco_image_folder, image_name)
 
    # 读取图像
    image = cv2.imread(image_path)
    height, width, channels = image.shape
 
    # 创建YOLO格式的注释文件
    yolo_annotation_file = os.path.join(yolo_folder, os.path.splitext(image_name)[0] + ".txt")
    with open(yolo_annotation_file, "w") as f:
        # 获取图像中的每个注释
        for annotation in coco_data['annotations']:
            # 如果注释属于当前图像，则将其转换为YOLO格式
            if annotation['image_id'] == image_data['id']:
                convert_annotation(image_data['id'], [annotation], f)
 
    # 将图像复制到YOLO数据集文件夹中
    yolo_image_path = os.path.join(yolo_folder, image_name)
    cv2.imwrite(yolo_image_path, image)

在运行完上述代码后，YOLO数据集文件夹中应该包括所有转换后的图像和注释文件。

coco数据集有什么类别

### COCO 数据集中的类别

COCO (Common Objects in Context) 数据集涵盖了广泛的常见物体类别，这些类别的定义有助于目标检测、实例分割以及关键点检测等多种计算机视觉任务。具体来说，在标准版本的 COCO 数据集中包含了80个不同的对象类别[^1]。

为了更直观展示这80种类别，以下是部分代表性类别的列表：

- **人物**：person
- **交通工具**：bicycle, car, motorcycle, airplane, bus, train, truck, boat
- **动物**：bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe
- **室内物品**：backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket
- **厨房用品**：bottle, wine glass, cup, fork, knife, spoon, bowl
- **食物**：banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake
- **家具**：chair, couch, potted plant, bed, dining table, toilet
- **电子设备**：tv, laptop, mouse, remote, keyboard, cell phone
- **家电**：microwave, oven, toaster, sink, refrigerator
- **书本与卡片**：book, clock, vase, scissors, teddy bear, hair drier, toothbrush

对于希望深入了解或使用该数据集的研究人员而言，可以利用官方提供的API工具来加载并探索完整的类别信息及其对应的标注细节[^2]。

from pycocotools.coco import COCO
import os

# 初始化COCO api用于实例注释
dataDir='.'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)

# 加载COCO数据集
coco=COCO(annFile)

# 获取所有类别ID
catIds = coco.getCatIds()

# 显示所有类别名称
cats = coco.loadCats(catIds)
print([cat['name'] for cat in cats])