首页 > 分享 > 使用YOLOv8进行目标检测花卉数据集任务，高效地处理数据和训练模型。花卉数据集 12种花共8000多张图片鲜花花数据集

使用YOLOv8进行目标检测花卉数据集任务，高效地处理数据和训练模型。花卉数据集 12种花共8000多张图片鲜花花数据集

花匠小妙招
2024-12-21 11:32

在这里插入图片描述
花卉数据集
12种花共8000多张图片

从数据准备、模型训练、评估和可视化等方面详细介绍整个流程。我们将使用YOLOv8进行目标检测任务，并提供一些常用的脚本来帮助你更高效地处理数据和训练模型。

1. 环境准备

首先，确保你已经安装了必要的库和工具。你可以使用以下命令安装所需的库：

pip install ultralytics pip install torch torchvision pip install opencv-python pip install pandas pip install matplotlib 12345 2. 数据准备

假设你的数据集目录结构如下：

flower_dataset/ ├── images/ │ ├── train/ │ └── val/ ├── labels/ │ ├── train/ │ └── val/ 1234567

每个图像文件和对应的标签文件都以相同的文件名命名，例如 0001.jpg 和 0001.txt。

3. 创建数据配置文件

创建一个名为flower_detection.yaml的文件，内容如下：

# Flower Detection Dataset Configuration # Path to the dataset directory path: ./flower_dataset # Training and validation image directories train: images/train val: images/val # Training and validation label directories train_labels: labels/train val_labels: labels/val # Number of classes nc: 12 # Class names names: 0: rose 1: tulip 2: daisy 3: sunflower 4: lily 5: orchid 6: dandelion 7: peony 8: carnation 9: iris 10: hyacinth 11: pansy 123456789101112131415161718192021222324252627282930 4. 训练模型

使用YOLOv8进行目标检测任务训练，可以使用以下命令：

yolo detect train data=flower_detection.yaml model=yolov8n.pt epochs=100 imgsz=512 1

解释：

data=flower_detection.yaml: 指定数据配置文件。model=yolov8n.pt: 使用预训练的YOLOv8检测模型（yolov8n）。你可以选择其他大小的模型，如yolov8s、yolov8m、yolov8l或yolov8x。epochs=100: 训练的轮数。imgsz=512: 图像的尺寸。 5. 评估模型

训练完成后，可以使用以下命令来评估模型在验证集上的性能：

yolo detect val data=flower_detection.yaml model=runs/detect/train/weights/best.pt imgsz=512 1

解释：

data=flower_detection.yaml: 指定数据配置文件。model=runs/detect/train/weights/best.pt: 指定训练过程中保存的最佳模型权重文件。imgsz=512: 图像的尺寸。 6. 可视化预测结果

使用以下Python代码来可视化模型的预测结果：

import cv2 import torch from ultralytics import YOLO # 加载模型 model = YOLO('runs/detect/train/weights/best.pt') # 读取图像 image_path = 'flower_dataset/images/val/0001.jpg' image = cv2.imread(image_path) # 进行预测 results = model(image) # 可视化预测结果 for result in results: boxes = result.boxes.xyxy confidences = result.boxes.conf class_ids = result.boxes.cls for box, conf, class_id in zip(boxes, confidences, class_ids): x1, y1, x2, y2 = map(int, box) label = model.names[int(class_id)] confidence = float(conf) # 绘制边界框 cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) text = f'{label}: {confidence:.2f}' cv2.putText(image, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # 显示图像 cv2.imshow('Detection Prediction', image) cv2.waitKey(0) cv2.destroyAllWindows() 12345678910111213141516171819202122232425262728293031323334 7. 常用的五个脚本 数据增强脚本：用于生成更多的训练数据。数据检查脚本：用于检查数据集的完整性和一致性。模型评估脚本：用于评估模型在不同指标上的性能。预测结果保存脚本：用于将预测结果保存为文件。模型推理脚本：用于在新的图像上进行推理。 1. 数据增强脚本

import os import cv2 from imgaug import augmenters as iaa def augment_image(image_path, output_dir, num_augmentations=5): image = cv2.imread(image_path) seq = iaa.Sequential([ iaa.Fliplr(0.5), # 水平翻转 iaa.Affine(rotate=(-10, 10)), # 旋转 iaa.Multiply((0.8, 1.2)) # 改变亮度 ]) for i in range(num_augmentations): augmented_image = seq(image=image) new_image_path = os.path.join(output_dir, f"{os.path.splitext(os.path.basename(image_path))[0]}_aug{i}.jpg") cv2.imwrite(new_image_path, augmented_image) # 数据集路径 image_dir = 'flower_dataset/images/train' output_dir = 'flower_dataset/augmented/train' os.makedirs(output_dir, exist_ok=True) # 获取所有图像文件 image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.jpg')] for image_file in image_files: augment_image(image_file, output_dir) 1234567891011121314151617181920212223242526272829 2. 数据检查脚本

import os def check_data_consistency(image_dir, label_dir): image_files = set(os.listdir(image_dir)) label_files = set(os.listdir(label_dir)) missing_labels = image_files - {f.replace('.txt', '.jpg') for f in label_files} missing_images = {f.replace('.txt', '.jpg') for f in label_files} - image_files if missing_labels: print("Missing labels for images:") for file in missing_labels: print(file) if missing_images: print("Missing images for labels:") for file in missing_images: print(file) if not missing_labels and not missing_images: print("Data consistency check passed.") # 数据集路径 image_dir = 'flower_dataset/images/train' label_dir = 'flower_dataset/labels/train' check_data_consistency(image_dir, label_dir) 123456789101112131415161718192021222324252627 3. 模型评估脚本

import torch from ultralytics import YOLO from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval def evaluate_model(model_path, data_config, ann_file): model = YOLO(model_path) coco = COCO(ann_file) results = [] for img_id in coco.getImgIds(): img_info = coco.loadImgs(img_id)[0] image_path = os.path.join(data_config['path'], img_info['file_name']) image = cv2.imread(image_path) predictions = model(image) for pred in predictions: boxes = pred.boxes.xyxy confidences = pred.boxes.conf class_ids = pred.boxes.cls for box, conf, class_id in zip(boxes, confidences, class_ids): x1, y1, x2, y2 = map(int, box) w, h = x2 - x1, y2 - y1 results.append({ "image_id": img_id, "category_id": int(class_id) + 1, "bbox": [x1, y1, w, h], "score": float(conf) }) with open('results.json', 'w') as f: json.dump(results, f) coco_dt = coco.loadRes('results.json') coco_eval = COCOeval(coco, coco_dt, 'bbox') coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() # 模型路径和数据配置 model_path = 'runs/detect/train/weights/best.pt' data_config = { 'path': './flower_dataset', 'annotations': 'annotations/instances_val.json' } ann_file = os.path.join(data_config['path'], data_config['annotations']) evaluate_model(model_path, data_config, ann_file) 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950 4. 预测结果保存脚本

import cv2 import torch from ultralytics import YOLO import json def save_predictions(model_path, image_dir, output_dir): model = YOLO(model_path) os.makedirs(output_dir, exist_ok=True) image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.jpg')] results = [] for image_file in image_files: image = cv2.imread(image_file) predictions = model(image) for pred in predictions: boxes = pred.boxes.xyxy confidences = pred.boxes.conf class_ids = pred.boxes.cls for box, conf, class_id in zip(boxes, confidences, class_ids): x1, y1, x2, y2 = map(int, box) w, h = x2 - x1, y2 - y1 results.append({ "image_path": image_file, "category_id": int(class_id), "bbox": [x1, y1, w, h], "score": float(conf) }) with open(os.path.join(output_dir, 'predictions.json'), 'w') as f: json.dump(results, f) # 模型路径和数据集路径 model_path = 'runs/detect/train/weights/best.pt' image_dir = 'flower_dataset/images/val' output_dir = 'flower_dataset/predictions' save_predictions(model_path, image_dir, output_dir) 12345678910111213141516171819202122232425262728293031323334353637383940 5. 模型推理脚本

import cv2 import torch from ultralytics import YOLO def infer_model(model_path, image_path): model = YOLO(model_path) image = cv2.imread(image_path) results = model(image) for result in results: boxes = result.boxes.xyxy confidences = result.boxes.conf class_ids = result.boxes.cls for box, conf, class_id in zip(boxes, confidences, class_ids): x1, y1, x2, y2 = map(int, box) label = model.names[int(class_id)] confidence = float(conf) # 绘制边界框 cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) text = f'{label}: {confidence:.2f}' cv2.putText(image, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # 显示图像 cv2.imshow('Inference', image) cv2.waitKey(0) cv2.destroyAllWindows() # 模型路径和图像路径 model_path = 'runs/detect/train/weights/best.pt' image_path = 'flower_dataset/images/val/0001.jpg' infer_model(model_path, image_path) 1234567891011121314151617181920212223242526272829303132333435 8. 总结

以上步骤提供了一个完整的框架，用于使用YOLOv8训练花卉数据集的目标检测任务。代码包括数据准备、模型训练、评估和结果可视化等多个步骤。希望这些代码对你有帮助！如果有任何问题或需要进一步的调整，请随时告诉我。