Explore Ultralytics YOLOv8

hey there! can you help me to handle error which i get every time when run train increasing batch and imgsz options, please? first of all, i have a powerful enough computer. RTX 3090 24gb, Ryzen 9 7950X3D 16-Core, 128gb RAM if i run train with following options results = model.train(data=f'{database_foldername}data.yaml', cfg=cfg_filename, epochs=epochs, imgsz=640, batch=8, device=0) it works quit good, and it uses about 3-4gb GPU_MEM, but i want to change imgsz to 1280 to increase accuracy of model and when i run train with this changed option i always got different errors, like: RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED RuntimeError: CUDA error: device-side assert triggered Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. RuntimeError: CUDA error: an illegal memory access was encountered Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. RuntimeError: Pin memory thread exited unexpectedly terminate called without an active exception

and as i wrote earlier it throw different every time. i tride run with CUDA_LAUNCH_BLOCKING=1, but nothing changed

help!!! i want to use all the power of my graphics card

Hi YOLO Team, I have a v8 segmentation model, i need to convert my pt file to TFlite, is it possible?

"# Improved YOLOv8 with BiFormer, Fusion Blocks, MS-Block, DConv, ECA, Ghost Module, and Focal Loss

Parameters

nc: 9 # number of classes scales:

[depth, width, max_channels]

n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]

Backbone (ensure specific feature sizes: 160×160×128, 80×80×256, 40×40×512, 20×20×512)

backbone:

[from, repeats, module, args]

• [-1, 1, GhostModule, [64, 3, 2]] # 0-P1/2 -> output 320×320×64, using Ghost Module to reduce computation
• [-1, 1, Conv, [128, 3, 2, Mish]] # 1-P2/4 -> output 160×160×128, using Mish activation
• [-1, 3, C2f_DM, [128, True]] # output 160×160×128
• [-1, 1, MS-Block, [128, 3, [3, 5, 7], True]] # MS-Block with residual connections for multi-scale feature extraction
• [-1, 1, Conv, [256, 3, 2, Mish]] # 3-P3/8 -> output 80×80×256, using Mish activation
• [-1, 6, C2f_DM, [256, True]] # output 80×80×256
• [-1, 1, MS-Block, [256, 3, [3, 5, 7], True]] # MS-Block for P3/8 with residual connections
• [-1, 1, Conv, [512, 3, 2, Mish]] # 5-P4/16 -> output 40×40×512, using Mish activation
• [-1, 6, C2f_DM, [512, True]] # output 40×40×512
• [-1, 1, MS-Block, [512, 3, [3, 5, 7], True]] # MS-Block for P4/16 with residual connections
• [-1, 1, Conv, [1024, 3, 2, Mish]] # 7-P5/32 -> output 20×20×512, using Mish activation
• [-1, 3, C2f_DM, [1024, True]] # output 20×20×512
• [-1, 1, MS-Block, [1024, 3, [3, 5, 7], True]] # MS-Block for P5/32 with residual connections
• [-1, 1, SPPF, [1024, 5]] # Spatial Pyramid Pooling Fast -> output 20×20×512

Neck with Fusion Blocks, MS-Block, BiFormer, DConv, and ECA (replacing Concat)

neck:

[from, repeats, module, args]

• [-1, 1, nn.Upsample, [None, 2, 'nearest']]

• [[-1, 6], 1, Fusion_Block, [512]] # Fusion Block replacing Concat for backbone P4

• [-1, 3, C2f_Att, [512]] # C2F-Att with Attention Mechanism

• [-1, 1, BiFormer, [512]] # BiFormer added for enhanced spatial relationships

• [-1, 1, DConv, [512, 3]] # Adding Dynamic Convolution

• [-1, 1, ECA, [512]] # Adding Efficient Channel Attention

• [-1, 1, MS-Block, [512, 3, [3, 5, 7], True]] # MS-Block for multi-scale feature extraction in Neck

• [-1, 1, nn.Upsample, [None, 2, 'nearest']]

• [[-1, 4], 1, Fusion_Block, [256]] # Fusion Block replacing Concat for backbone P3

• [-1, 3, C2f_Att, [256]] # P3/8-small

• [-1, 1, BiFormer, [256]] # BiFormer added for P3

• [-1, 1, DConv, [256, 3]] # Adding Dynamic Convolution for P3

• [-1, 1, ECA, [256]] # Adding Efficient Channel Attention for P3

• [-1, 1, MS-Block, [256, 3, [3, 5, 7], True]] # MS-Block for P3 in Neck

• [-1, 1, Conv, [256, 3, 2, Mish]]

• [[-1, 12], 1, Fusion_Block, [512]] # Fusion Block replacing Concat for head P4

• [-1, 3, C2f_Att, [512]] # P4/16-medium

• [-1, 1, BiFormer, [512]] # BiFormer added for P4

• [-1, 1, DConv, [512, 3]] # Adding Dynamic Convolution for P4

• [-1, 1, ECA, [512]] # Adding Efficient Channel Attention for P4

• [-1, 1, MS-Block, [512, 3, [3, 5, 7], True]] # MS-Block for P4 in Neck

• [-1, 1, Conv, [512, 3, 2, Mish]]

• [[-1, 9], 1, Fusion_Block, [1024]] # Fusion Block replacing Concat for head P5

• [-1, 3, C2f_Att, [1024]] # P5/32-large

• [-1, 1, BiFormer, [1024]] # BiFormer added for P5

• [-1, 1, DConv, [1024, 3]] # Adding Dynamic Convolution for P5

• [-1, 1, ECA, [1024]] # Adding Efficient Channel Attention for P5

• [-1, 1, MS-Block, [1024, 3, [3, 5, 7], True]] # MS-Block for P5 in Neck

Adding a fourth detecting head for improved detection at different scales

• [-1, 1, Conv, [128, 3, 2, Mish]]
• [[-1, 3], 1, Fusion_Block, [128]] # new Fusion Block for smaller objects
• [-1, 3, C2f_Att, [128]] # P6/64-smaller objects
• [-1, 1, BiFormer, [128]] # BiFormer added for P6
• [-1, 1, DConv, [128, 3]] # Adding Dynamic Convolution for P6
• [-1, 1, ECA, [128]] # Adding Efficient Channel Attention for P6
• [-1, 1, MS-Block, [128, 3, [3, 5, 7], True]] # MS-Block for P6 in Neck

Head

head:

• [[17, 21, 25, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

Loss Function

loss: name: FocalLoss # Using Focal Loss for better handling of small object detection" If I change the new structure like this, then I can add code for new modules, blocks: GhostModule, C2f_DM, MS-Block, BiFormer, .... to which files?

有没有人能为我解答一下，训练数据集过程中的权重文件也能用吗？为什么训练过程中的权重文件比训练结束后的大？

作者您好，我想训练yolov8m-p2的版本，我在想能否使用yolov8的模型权重作为预训练权重，且我发现似乎没有yolov8-p2的m的版本这个怎末决解呢

作者你好，请问海滩或者足球场，怎么才能提高对人的识别率？

感谢yolov8让我可以研究生毕业

I came to know that YOLOv8 is capable of tracking objects. I am currently working on a robotics model that includes object detection and object tracking. Can YOLOv8 perform both detection and tracking simultaneously? Also, you didn’t mention the benchmark for object tracking compared to other models.

I am trying to use yolov8m.pt's backbone for feature extractor. Why does it force download the coco dataset? Is there a way I could just load the weights rather downloading the data? I need to extract the features from custom dataset

Hi，dears, i'm new of YoLoV8, can you help see is there any thing can be improved of my below code?

from PIL import Image from ultralytics import YOLO import glob import os import numpy as np from tqdm import tqdm import torch from concurrent.futures import ThreadPoolExecutor

class Predict def init(self, batch_size=8, imgsz=640, conf_thres=0.25, iou_thres=0.45, half=True, device=cuda0)

    初始化优化后的推理类
    param batch_size 批量推理大小（根据GPU显存调整）
    param imgsz 推理图片尺寸（建议为32的倍数）
    param conf_thres 置信度阈值（过滤低置信度预测）
    param iou_thres NMS的IOU阈值（控制重复框）
    param half 是否使用半精度推理（FP16，提速且不损失精度）
    param device 推理设备（cudacpumps）
    
    self.model_path = homejovyanworkstudentmodelsyolo_train4weightsbest.pt
    self.device = torch.device(device if torch.cuda.is_available() else cpu)
    self.model = YOLO(self.model_path).to(self.device)
    
    # 核心推理参数
    self.batch_size = batch_size
    self.imgsz = imgsz
    self.conf_thres = conf_thres
    self.iou_thres = iou_thres
    self.half = half and torch.cuda.is_available()  # 仅GPU支持半精度
    
    # 预热模型（首次推理加速）
    self._warmup_model()

def _warmup_model(self)
    模型预热：首次推理避免初始化耗时
    dummy_img = torch.randn(1, 3, self.imgsz, self.imgsz).to(self.device)
    if self.half
        dummy_img = dummy_img.half()
    self.model.predict(dummy_img, verbose=False, conf=self.conf_thres, iou=self.iou_thres, half=self.half)

def _load_image(self, img_path)
    单张图片加载（封装为线程池调用）
    try
        with Image.open(img_path) as img
            img = img.convert(RGB)
            return img_path, img, None
    except Exception as e
        return img_path, None, str(e)

def _batch_load_images(self, image_paths)
    批量加载图片（多线程提速）
    images = []
    valid_paths = []
    error_logs = []
    
    # 多线程加载图片（CPU密集型任务提速）
    with ThreadPoolExecutor(max_workers=4) as executor
        results = list(tqdm(executor.map(self._load_image, image_paths), total=len(image_paths), desc=Loading images))
    
    for path, img, err in results
        if err
            error_logs.append(fSkipping {path} {err})
            continue
        valid_paths.append(path)
        images.append(img)
    
    # 打印加载错误
    if error_logs
        for err in error_logs[5]  # 只打印前5个错误
            print(err)
    return valid_paths, images

def detect(self, image_dir)
    
    优化后的推理主函数
    param image_dir 图片目录
    param use_tta 是否启用TTA（测试时增强，提升准确率，小幅增加耗时）
    return 推理结果列表
    
    # 1. 获取所有图片路径
    exts = [.jpg, .jpeg, .png, .JPG, .JPEG, .PNG]
    image_paths = []
    for ext in exts
        image_paths.extend(glob.glob(os.path.join(image_dir, ext)))
    if not image_paths
        print(No images found in directory!)
        return []

    # 2. 批量加载图片（多线程）
    valid_paths, images = self._batch_load_images(image_paths)
    if not valid_paths
        return []

    # 3. 分批次推理
    all_results = []
    num_batches = (len(valid_paths) + self.batch_size - 1)  self.batch_size
    
    for batch_idx in tqdm(range(num_batches), desc=Inferencing batches)
        # 切分批次
        start = batch_idx  self.batch_size
        end = min((batch_idx + 1)  self.batch_size, len(valid_paths))
        batch_paths = valid_paths[startend]
        batch_imgs = images[startend]

        # 4. 执行推理（核心优化）
        try
            results = self.model.predict(
                source=batch_imgs,
                imgsz=self.imgsz,          # 统一推理尺寸
                conf=self.conf_thres,      # 置信度过滤
                iou=self.iou_thres,        # NMS阈值
                half=self.half,            # 半精度推理
                device=self.device,        # 指定设备
                verbose=False,             # 关闭日志
                batch=self.batch_size,     # 批量大小
              #  tta=use_tta,               # 测试时增强（可选）
                max_det=100,               # 单图最大检测数
                vid_stride=1,              # 视频步长（图片无效）
                retina_masks=False         # 掩码细化（无掩码时关闭）
            )
        except Exception as e
            print(fBatch {batch_idx} inference failed {e})
            continue

        # 5. 解析结果（减少CPUGPU数据拷贝）
        for idx, result in enumerate(results)
            img_path = batch_paths[idx]
            image_name = os.path.basename(img_path)
            detections = []

            if result.boxes is not None and len(result.boxes)  0
                # 批量转换数据（避免逐框循环拷贝）
                cls = result.boxes.cls.cpu().numpy().astype(int).tolist()
                boxes = result.boxes.xyxy.cpu().numpy().tolist()
                scores = result.boxes.conf.cpu().numpy().astype(float).tolist()

                # 生成检测结果
                for c, b, s in zip(cls, boxes, scores)
                    detections.append({
                        cls int(c),
                        box [float(x) for x in b],
                     score round(float(s), 3)  # 保留3位小数，减少存储
                    })

            all_results.append({
                image_name image_name,
                detections detections,
                image_path img_path
            })

    return all_results

使用示例

if name == main # 初始化优化后的推理器（根据GPU显存调整参数） pre = Predict( batch_size=16, # 批量大小（1080Ti建议8-16，A100建议32-64） imgsz=640, # 推理尺寸（训练时用640则保持一致） conf_thres=0.3, # 提高置信度阈值，过滤假阳性 iou_thres=0.4, # 降低IOU阈值，减少重复框 half=True, # 启用半精度推理 device=cuda0 # 指定GPU )

# 执行推理（use_tta=True 提升准确率，耗时增加~30%）
results = pre.detect(image_dir=homejovyanworkstudenttest)

# 打印结果
for idx, result in enumerate(results[3])  # 只打印前3个
    print(fImage {result['image_name']})
    print(fDetections {len(result['detections'])} objects)
    for det in result[detections]
        print(f  - Class {det['cls']}, Box {det['box']}, Score {det['score']})
    print(-  50)

print(fTotal processed images {len(results)})