YOLOv8小目標TinyPerson 行人數(shù)據(jù)集識別 行人小目標檢測 包括數(shù)據(jù)準備、模型訓練、評估和推理。小目標檢測Tinyperson數(shù)據(jù)集。其train71748以及test78130處理好的yolo格式(txt)以及voc格式(xml)標簽訓練時可自行混合后按比例劃分訓練集和驗證集。標簽類別0:earth_person1:sea_person包括數(shù)據(jù)準備、模型訓練、評估和可視化。整個代碼塊在一個 artifact 中方便一次性復制。完整代碼importosimportcv2importnumpy as np from sklearn.model_selectionimporttrain_test_splitimportshutil from ultralyticsimportYOLOimportmatplotlib.pyplot as plt# Define pathsdata_pathpath_to_TinyPersonimages_train_pathos.path.join(data_path,images,train)images_test_pathos.path.join(data_path,images,test)labels_yolo_train_pathos.path.join(data_path,labels_yolo,train)labels_yolo_test_pathos.path.join(data_path,labels_yolo,test)# Create directories if they dont existos.makedirs(images_train_path,exist_okTrue)os.makedirs(images_test_path,exist_okTrue)os.makedirs(labels_yolo_train_path,exist_okTrue)os.makedirs(labels_yolo_test_path,exist_okTrue)# Combine train and test sets for splitting into train, validation, and testall_images[]all_labels[]forfilenameinos.listdir(images_train_path): image_filenamefilename label_filenameos.path.splitext(filename)[0].txtimage_pathos.path.join(images_train_path, image_filename)label_pathos.path.join(labels_yolo_train_path, label_filename)all_images.append(image_path)all_labels.append(label_path)forfilenameinos.listdir(images_test_path): image_filenamefilename label_filenameos.path.splitext(filename)[0].txtimage_pathos.path.join(images_test_path, image_filename)label_pathos.path.join(labels_yolo_test_path, label_filename)all_images.append(image_path)all_labels.append(label_path)# Split data into train, validation, and test setstrain_images, temp_images, train_labels, temp_labelstrain_test_split(all_images, all_labels,test_size0.3,random_state42)val_images, test_images, val_labels, test_labelstrain_test_split(temp_images, temp_labels,test_size0.5,random_state42)# Move files to respective foldersdef move_files(images, labels, dest_image_folder, dest_label_folder):forimage_path, label_pathinzip(images, labels): shutil.move(image_path, dest_image_folder)shutil.move(label_path, dest_label_folder)move_files(train_images, train_labels, os.path.join(data_path,images,train), os.path.join(data_path,labels_yolo,train))move_files(val_images, val_labels, os.path.join(data_path,images,val), os.path.join(data_path,labels_yolo,val))move_files(test_images, test_labels, os.path.join(data_path,images,test), os.path.join(data_path,labels_yolo,test))# Create dataset.yaml file for YOLOv8dataset_yaml_content train: ./images/train val: ./images/val test: ./images/test nc:2names:[earth_person,sea_person] with open(os.path.join(data_path,dataset.yaml),w)as f: f.write(dataset_yaml_content)# Step 3: Train YOLOv8 Model# Load a pre-trained YOLOv8 modelmodelYOLO(yolov8n.pt)# You can choose other sizes like yolov8s, yolov8m, yolov8l, yolov8x# Modify the number of classes in the final layermodel.nc2# Training commandresultsmodel.train(dataos.path.join(data_path,dataset.yaml),imgsz640,epochs50,batch16,devicecudaiftorch.cuda.is_available()elsecpu,cacheTrue)# Evaluate the modelmetricsmodel.val()# Export the trained modelmodel.export(formatonnx)# Inference using the trained model# Load the trained modelinference_modelYOLO(runs/detect/train/weights/best.pt)# Path to your best weights# Perform inference on a sample imagesample_image_pathos.path.join(data_path,images,test,sample_image.jpg)# Replace with your sample image pathresultsinference_model(sample_image_path)# Visualize resultsdef plot_results(results, image_path): imagecv2.imread(image_path)imagecv2.cvtColor(image, cv2.COLOR_BGR2RGB)forresultinresults: boxesresult.boxes.cpu().numpy()forboxinboxes: rbox.xyxy[0].astype(int)clsint(box.cls[0])confbox.conf[0]cv2.rectangle(image,(r[0], r[1]),(r[2], r[3]),(0,255,0),2)cv2.putText(image, f{result.names[cls]} {conf:.2f},(r[0], r[1]-10), cv2.FONT_HERSHEY_SIMPLEX,0.9,(0,255,0),2)plt.figure(figsize(10,10))plt.imshow(image)plt.axis(off)plt.show()plot_results(results, sample_image_path)運行腳本在終端中運行以下命令來執(zhí)行整個流程python main.py總結以上文檔包含了從數(shù)據(jù)加載、預處理、模型構建到訓練、評估和可視化的所有步驟。希望這些詳細的信息和代碼能夠幫助你順利實施和優(yōu)化你的 TinyPerson 小目標檢測系統(tǒng)。如果你有任何進一步的問題或需要更多幫助請隨時提問自定義說明數(shù)據(jù)文件路徑: 修改data_path變量以指向你的 TinyPerson 數(shù)據(jù)集路徑。圖像分辨率: 根據(jù)需要調整數(shù)據(jù)增強中的圖像大小例如imgsz640。超參數(shù)調整: 根據(jù)需要調整訓練參數(shù)如epochs,batch_size等。模型選擇: 你可以選擇不同的 YOLOv8 模型大小yolov8n,yolov8s,yolov8m,yolov8l,yolov8x以適應你的需求。推理樣本路徑: 修改sample_image_path變量以指向你要進行推理的圖片路徑。通過這些步驟你可以靈活地使用 TinyPerson 數(shù)據(jù)集進行小目標檢測任務。