详解yolov8的nms中multi-label功能为什么不是真正的multi-label任务实现

文章探讨了YOLOv8中的非极大抑制(NMS)在多标签分类任务中的应用,指出NMS本质上并非真正的多标签网络。同时,详细解释了v8的lossfunction计算过程,尤其是TaskAlignedAssigner在样本分配和loss计算中的关键作用,强调了模型对每个像素最大面积真实目标框的依赖,限制了对重叠多类别框的训练能力。

一、什么是multi-label?

多标签分类(Multilabel classification): 给每个样本一系列的目标标签,即表示的是样本各属性而不是相互排斥的。比如图片中的猫可同时拥有两个标签cat、animal,需要预测出一个概念集合。

2.一般思路如何实现multi-label任务?

要实现这个任务,一种是使用多个模型,可以并行使用两个模型分别预测同一个物体,每个模型对该物体的预测不同。即一个模型预测图片中的猫为cat,另一个预测其为animal。这种方法比较简单实用,但可能满足不了一些场合的单一模型推理要求。

一种是专门设计一个网络同时对物体带有的多个标签进行训练,设计思路:
1.从网络的数据集、输入、损失函数、标签分配策略进行修改。
2.类似multi-task网络的形式,对网络输出做分支并行。

(两种实现方法并不是本文所讲述主题,一言带过~)

二、yolov8中nms函数的multi-label

首先放一段v8中nms源码

def non_max_suppression(
        prediction,
        conf_thres=0.25,
        iou_thres=0.45,
        classes=None,
        agnostic=False,
        multi_label=False,
        labels=(),
        max_det=300,
        nc=0,  # number of classes (optional)
        max_time_img=0.05,
        max_nms=30000,
        max_wh=7680,
):
    """
    Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.

    Args:
        prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
            containing the predicted boxes, classes, and masks. The tensor should be in the format
            output by a model, such as YOLO.
        conf_thres (float): The confidence threshold below which boxes will be filtered out.
            Valid values are between 0.0 and 1.0.
        iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.
            Valid values are between 0.0 and 1.0.
        classes (List[int]): A list of class indices to consider. If None, all classes will be considered.
        agnostic (bool): If True, the model is agnostic to the number of classes, and all
            classes will be considered as one.
        multi_label (bool): If True, each box may have multiple labels.
        labels (List[List[Union[int, float, torch.Tensor]]]): A list of lists, where each inner
            list contains the apriori labels for a given image. The list should be in the format
            output by a dataloader, with each label being a tuple of (class_index, x1, y1, x2, y2).
        max_det (int): The maximum number of boxes to keep after NMS.
        nc (int, optional): The number of classes output by the model. Any indices after this will be considered masks.
        max_time_img (float): The maximum time (seconds) for processing one image.
        max_nms (int): The maximum number of boxes into torchvision.ops.nms().
        max_wh (int): The maximum box width and height in pixels

    Returns:
        (List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
            shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns
            (x1, y1, x2, y2, confidence, class, mask1, mask2, ...).
    """

    # Checks
    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
    if isinstance(prediction, (list, tuple)):  # YOLOv8 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    device = prediction.device
    mps = 'mps' in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    nc = nc or (prediction.shape[1] - 4)  # number of classes
    nm = prediction.shape[1] - nc - 4
    mi = 4 + nc  # mask
<think> 我们正在将YOLOv8模型修改为YOLOv8-seg(实例分割模型)以适配单通道float32 TIFF图像(范围0.0~1.0)。主要修改点包括: 1. 修改模型输入层为单通道(与之前类似) 2. 修改数据集加载器以支持分割任务的标注(包括目标检测框和分割掩码) 3. 调整损失函数以处理分割任务YOLOv8-seg已经包含分割头,所以我们主要关注数据加载和输入适配) 注意:YOLOv8-seg的输出有两个部分:检测输出(框和类别)和分割输出(掩码原型和系数)。因此,我们的数据集需要同时提供边界框和分割掩码。 步骤: 1. 自定义数据集类,加载TIFF图像以及对应的标签(包括边界框和分割掩码)。 2. 修改模型的第一层卷积,使其接受单通道输入。 3. 调整数据增强流程,确保同时增强图像、边界框和分割掩码。 4. 训练时使用YOLOv8-seg的损失函数(包含检测损失和分割损失)。 由于问题中图像是单通道float32且范围0.0~1.0,我们在数据加载时不需要改变数据类型,但需要注意: - 分割掩码通常为二值图像(0和1)或多类别整数标签图(对于实例分割,每个实例一个唯一的整数,背景为0)。 -YOLOv8-seg中,分割标注通常以多边形坐标的形式存储(在标签文件中),或者我们可以直接提供掩码图像。 这里我们假设标注格式为: - 边界框:YOLO格式的txt文件(class, x_center, y_center, width, height) - 分割掩码:与图像同名的PNG文件(或TIFF),每个实例用不同的整数标记(0表示背景)。 但注意,YOLOv8-seg的训练数据标注格式与检测任务类似,但额外包括分割多边形的坐标。具体来说,标签文件中的每一行代表一个实例,格式为: class_id x1 y1 x2 y2 ... xn yn (多边形坐标归一化) 或者,也可以使用单独的分割掩码文件。 为了简化,我们使用两种方式: 方式1:使用单独的分割掩码文件(每个图像对应一个掩码文件,每个实例用不同的整数标记)。 方式2:在标签文件中同时包含边界框和多边形点(YOLO官方格式)。 我们选择方式2,因为YOLOv8-seg官方支持这种方式。因此,每个标签文件的内容为: class_id x1 y1 x2 y2 ... xn yn 其中,多边形坐标是归一化的(相对于图像宽高)。 但是,我们的数据集加载器需要同时提供边界框和分割多边形。因此,在数据加载时,我们: - 从标签文件中读取每个实例的类别和多边形点 - 将多边形点转换为边界框(通过计算多边形的外接矩形) - 同时保留多边形点用于分割损失计算 另外,我们也可以直接使用边界框的标注(如果有单独的边界框标注文件),但为了统一,我们假设标签文件已经包含多边形点。 修改后的数据集类需要返回: - 图像(单通道float32,0-1范围) - 边界框(归一化的xywh)和类别 - 实例的多边形点(归一化坐标) 然而,在YOLOv8-seg中,训练时实际上不需要原始的掩码图像,而是需要多边形点。因此,我们只需读取标签文件中的多边形点即可。 具体实现: </think> # 适配单通道float32 TIFF图像的YOLOv8-seg修改方案 我将提供完整的解决方案,将YOLOv8模型修改为YOLOv8-seg(实例分割模型),同时适配单通道float32 TIFF图像(像素范围0.0~1.0)。 ## 完整的YOLOv8-seg修改方案 ### 1. 自定义数据集加载器(支持分割标注) ```python import os import numpy as np import torch import cv2 from tifffile import imread from torch.utils.data import Dataset import albumentations as A from albumentations.pytorch import ToTensorV2 import json class TiffSegmentationDataset(Dataset): """支持实例分割的自定义TIFF数据集""" def __init__(self, img_dir, label_dir, mask_dir, img_size=640, augment=True): """ :param img_dir: TIFF图像目录 :param label_dir: 检测标注目录 (YOLO格式) :param mask_dir: 分割掩码目录 (PNG或TIFF格式) :param img_size: 模型输入尺寸 :param augment: 是否应用数据增强 """ self.img_dir = img_dir self.label_dir = label_dir self.mask_dir = mask_dir self.img_size = img_size self.augment = augment # 获取所有TIFF文件 self.img_files = [f for f in os.listdir(img_dir) if f.lower().endswith(('.tif', '.tiff'))] # 数据增强管道 self.transform = self.build_transforms() # 缓存图像信息 self.imgs = [] self.labels = [] self.masks = [] self.shapes = [] self.load_all_data() def load_all_data(self): """预加载所有图像、标注和掩码""" print(f"Loading {len(self.img_files)} TIFF images with segmentation masks...") for img_file in self.img_files: # 加载TIFF图像 img_path = os.path.join(self.img_dir, img_file) img = imread(img_path).astype(np.float32) # 确保单通道格式 if img.ndim > 2: img = img[:, :, 0] # 取第一个通道 img = img[..., np.newaxis] # 添加通道维度 (H, W, 1) # 加载检测标签 label_file = img_file.replace('.tif', '.txt').replace('.tiff', '.txt') label_path = os.path.join(self.label_dir, label_file) labels = [] if os.path.exists(label_path): with open(label_path, 'r') as f: for line in f: line = line.strip().split() if line: # 转换为float: [class, x_center, y_center, width, height] labels.append([float(x) for x in line]) # 加载分割掩码 mask_file = img_file.replace('.tif', '.png').replace('.tiff', '.png') mask_path = os.path.join(self.mask_dir, mask_file) if os.path.exists(mask_path): # 读取为灰度图 mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE).astype(np.float32) mask = mask[..., np.newaxis] # (H, W, 1) else: mask = np.zeros(img.shape[:2] + (1,), dtype=np.float32) # 存储数据 self.imgs.append(img) self.shapes.append(img.shape[:2]) # (H, W) self.labels.append(np.array(labels, dtype=np.float32) if labels else np.zeros((0, 5))) self.masks.append(mask) def build_transforms(self): """构建数据增强管道(支持分割掩码)""" # 基础转换 transforms = [ A.Resize(self.img_size, self.img_size, always_apply=True), ToTensorV2(transpose_mask=True) ] # 训练时添加增强 if self.augment: transforms = [ # 几何变换(同时应用于图像和掩码) A.HorizontalFlip(p=0.5), A.VerticalFlip(p=0.5), A.Rotate(limit=10, p=0.5), A.RandomResizedCrop(self.img_size, self.img_size, scale=(0.8, 1.0), ratio=(0.8, 1.2), p=0.5), # 像素级变换(仅应用于图像) A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5), A.GaussianBlur(blur_limit=(3, 7), p=0.3), A.GaussNoise(var_limit=(10.0, 50.0), mean=0, p=0.3), # 确保最后是尺寸调整和张量转换 *transforms ] return A.Compose( transforms, bbox_params=A.BboxParams( format='yolo', min_visibility=0.4, label_fields=['class_labels'] ) if self.augment else None, additional_targets={'mask': 'mask'} ) def __len__(self): return len(self.img_files) def __getitem__(self, idx): img = self.imgs[idx] labels = self.labels[idx] mask = self.masks[idx] shape = self.shapes[idx] # 准备增强参数 transform_kwargs = {'image': img, 'mask': mask} if self.augment and len(labels) > 0: # 准备边界框和类别标签 bboxes = labels[:, 1:] class_labels = labels[:, 0] transform_kwargs['bboxes'] = bboxes transform_kwargs['class_labels'] = class_labels # 应用转换 transformed = self.transform(**transform_kwargs) img_tensor = transformed['image'] mask_tensor = transformed['mask'] # 处理增强后的边界框 if self.augment and len(labels) > 0: bboxes = np.array(transformed['bboxes']) class_labels = np.array(transformed['class_labels']) labels = np.column_stack([class_labels, bboxes]) # 转换为YOLO格式: (class, x_center, y_center, width, height) label_tensor = torch.zeros((len(labels), 6)) if len(labels) > 0: label_tensor[:, 1:] = torch.from_numpy(labels) return img_tensor, label_tensor, mask_tensor, torch.tensor(idx) ``` ### 2. 修改YOLOv8-seg模型输入层 ```python import torch import torch.nn as nn from ultralytics import YOLO def adapt_yolov8seg_for_single_channel(model_path='yolov8n-seg.pt'): """ 修改YOLOv8-seg模型以适配单通道输入 :param model_path: 预训练模型路径 :return: 修改后的模型 """ # 加载原始模型 model = YOLO(model_path) model = model.model # 获取底层PyTorch模型 # 查找第一个卷积层 first_conv = None for name, module in model.named_modules(): if isinstance(module, nn.Conv2d): first_conv = module break if first_conv is None: raise RuntimeError("未能在模型中找到卷积层") print(f"Original first conv layer: {first_conv}") # 创建新的单通道卷积层 new_conv = nn.Conv2d( in_channels=1, # 单通道输入 out_channels=first_conv.out_channels, kernel_size=first_conv.kernel_size, stride=first_conv.stride, padding=first_conv.padding, bias=first_conv.bias is not None ) # 转换权重:将RGB三通道权重取平均 with torch.no_grad(): original_weight = first_conv.weight.data # 原始形状: [out_channels, 3, kH, kW] # 新形状: [out_channels, 1, kH, kW] new_weight = original_weight.mean(dim=1, keepdim=True) new_conv.weight.data.copy_(new_weight) # 复制偏置(如果有) if first_conv.bias is not None: new_conv.bias.data.copy_(first_conv.bias.data) # 替换模型中的第一层卷积 # 需要找到模块的父模块和名称 parent_module = None conv_name = None for name, module in model.named_modules(): if module is first_conv: # 解析层级结构 parts = name.split('.') parent_module = model for part in parts[:-1]: parent_module = getattr(parent_module, part) conv_name = parts[-1] break if parent_module is None or conv_name is None: raise RuntimeError("无法定位卷积层的父模块") setattr(parent_module, conv_name, new_conv) print("Successfully adapted YOLOv8-seg model for single-channel input") return model ``` ### 3. 自定义训练循环(支持分割任务) ```python import torch from torch.utils.data import DataLoader from torch.optim import AdamW from torch.optim.lr_scheduler import CosineAnnealingLR from ultralytics.yolo.utils.loss import v8SegmentationLoss def train_yolov8seg_tiff(): # 配置参数 config = { 'data': { 'train_img': 'path/to/train/images', 'train_label': 'path/to/train/labels', 'train_mask': 'path/to/train/masks', 'val_img': 'path/to/val/images', 'val_label': 'path/to/val/labels', 'val_mask': 'path/to/val/masks' }, 'model': 'yolov8n-seg_single_channel.pt', # 修改后的模型 'epochs': 100, 'batch_size': 8, # 分割任务需要更多内存 'img_size': 640, 'mask_size': 160, # 分割掩码下采样尺寸 'device': 'cuda:0' if torch.cuda.is_available() else 'cpu', 'lr0': 0.001, 'weight_decay': 0.0005, 'save_dir': 'runs/train-seg', 'name': 'yolov8seg_tiff' } # 准备数据集 train_dataset = TiffSegmentationDataset( img_dir=config['data']['train_img'], label_dir=config['data']['train_label'], mask_dir=config['data']['train_mask'], img_size=config['img_size'], augment=True ) val_dataset = TiffSegmentationDataset( img_dir=config['data']['val_img'], label_dir=config['data']['val_label'], mask_dir=config['data']['val_mask'], img_size=config['img_size'], augment=False ) # 数据加载器 train_loader = DataLoader( train_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=4, pin_memory=True, collate_fn=seg_collate_fn ) val_loader = DataLoader( val_dataset, batch_size=config['batch_size'], shuffle=False, num_workers=2, pin_memory=True, collate_fn=seg_collate_fn ) # 加载模型 device = torch.device(config['device']) model = torch.load(config['model'], map_location=device) model.to(device) model.train() # 损失函数 (包含检测和分割损失) loss_fn = v8SegmentationLoss(model) # 优化器 optimizer = AdamW( model.parameters(), lr=config['lr0'], weight_decay=config['weight_decay'] ) # 学习率调度器 scheduler = CosineAnnealingLR( optimizer, T_max=config['epochs'] * len(train_loader), eta_min=config['lr0'] * 0.01 ) # 训练循环 for epoch in range(config['epochs']): model.train() train_loss = 0.0 train_det_loss = 0.0 train_seg_loss = 0.0 for batch_idx, (imgs, labels, masks, _) in enumerate(train_loader): imgs = imgs.to(device, non_blocking=True) labels = labels.to(device) masks = masks.to(device) # 调整掩码尺寸以匹配分割头输出 masks = F.interpolate(masks, size=config['mask_size'], mode='nearest') # 前向传播 preds, seg_preds = model(imgs) # 计算损失 (包含检测和分割损失) loss, loss_items = loss_fn(preds, labels, seg_preds, masks) det_loss = loss_items['det_loss'] seg_loss = loss_items['seg_loss'] # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step() scheduler.step() # 累计损失 train_loss += loss.item() train_det_loss += det_loss.item() train_seg_loss += seg_loss.item() # 每10个batch打印一次 if batch_idx % 10 == 0: print(f"Epoch: {epoch+1}/{config['epochs']} | " f"Batch: {batch_idx}/{len(train_loader)} | " f"Loss: {loss.item():.4f} | " f"Det Loss: {det_loss.item():.4f} | " f"Seg Loss: {seg_loss.item():.4f}") # 计算平均训练损失 avg_train_loss = train_loss / len(train_loader) avg_det_loss = train_det_loss / len(train_loader) avg_seg_loss = train_seg_loss / len(train_loader) # 验证 val_loss, val_det_loss, val_seg_loss = validate_seg(model, val_loader, loss_fn, device, config['mask_size']) print(f"Epoch {epoch+1} summary: " f"Train Loss: {avg_train_loss:.4f} (Det: {avg_det_loss:.4f}, Seg: {avg_seg_loss:.4f}) | " f"Val Loss: {val_loss:.4f} (Det: {val_det_loss:.4f}, Seg: {val_seg_loss:.4f})") # 保存模型 if (epoch + 1) % 10 == 0: save_path = os.path.join( config['save_dir'], config['name'], f'epoch_{epoch+1}.pt' ) torch.save(model.state_dict(), save_path) print(f"Model saved to {save_path}") print("Training completed!") return model def seg_collate_fn(batch): """自定义批处理函数(支持分割掩码)""" imgs, labels, masks, idxs = zip(*batch) return torch.stack(imgs, 0), torch.cat(labels, 0), torch.stack(masks, 0), torch.stack(idxs, 0) def validate_seg(model, val_loader, loss_fn, device, mask_size): """在验证集上评估分割模型""" model.eval() total_loss = 0.0 total_det_loss = 0.0 total_seg_loss = 0.0 with torch.no_grad(): for imgs, labels, masks, _ in val_loader: imgs = imgs.to(device, non_blocking=True) labels = labels.to(device) masks = masks.to(device) # 调整掩码尺寸 masks = F.interpolate(masks, size=mask_size, mode='nearest') # 前向传播 preds, seg_preds = model(imgs) # 计算损失 loss, loss_items = loss_fn(preds, labels, seg_preds, masks) det_loss = loss_items['det_loss'] seg_loss = loss_items['seg_loss'] # 累计损失 total_loss += loss.item() total_det_loss += det_loss.item() total_seg_loss += seg_loss.item() avg_loss = total_loss / len(val_loader) avg_det_loss = total_det_loss / len(val_loader) avg_seg_loss = total_seg_loss / len(val_loader) model.train() return avg_loss, avg_det_loss, avg_seg_loss ``` ### 4. TIFF图像分割推理函数 ```python import numpy as np import torch import torch.nn.functional as F from tifffile import imread, imsave def predict_segmentation_on_tiff(model, image_path, conf_thres=0.25, iou_thres=0.5): """ 在TIFF图像上进行实例分割预测 :param model: 训练好的YOLOv8-seg模型 :param image_path: TIFF图像路径 :param conf_thres: 置信度阈值 :param iou_thres: IoU阈值 :return: 检测结果, 分割掩码 """ # 读取TIFF图像 img = imread(image_path).astype(np.float32) # 确保单通道格式 if img.ndim > 2: img = img[:, :, 0] # 取第一个通道 img = img[..., np.newaxis] # (H, W, 1) # 预处理 original_shape = img.shape[:2] img_tensor = preprocess_image(img, model.img_size) img_tensor = img_tensor.unsqueeze(0) # 添加批次维度 # 推理 model.eval() with torch.no_grad(): preds, proto = model(img_tensor) detections, masks = process_segmentation_output( preds, proto, conf_thres, iou_thres ) # 转换到原始图像坐标 scale = min(model.img_size / original_shape[0], model.img_size / original_shape[1]) pad_x = (model.img_size - original_shape[1] * scale) / 2 pad_y = (model.img_size - original_shape[0] * scale) / 2 if detections is not None: detections[:, [0, 2]] -= pad_x # x坐标 detections[:, [1, 3]] -= pad_y # y坐标 detections[:, :4] /= scale # 处理分割掩码 if masks is not None and len(masks) > 0: # 调整掩码尺寸到原始图像 masks = F.interpolate(masks.unsqueeze(0), size=original_shape, mode='bilinear')[0] masks = masks.cpu().numpy() masks = (masks > 0.5).astype(np.uint8) # 二值化 return detections, masks def process_segmentation_output(preds, proto, conf_thres, iou_thres): """ 处理YOLOv8-seg的输出 """ # 非极大值抑制 (简化实现) from ultralytics.yolo.utils.ops import non_max_suppression # 应用NMS获取检测结果 detections = non_max_suppression( preds, conf_thres, iou_thres, multi_label=True )[0] # 取批次中的第一个结果 if detections is None: return None, None # 处理分割掩码 masks = process_proto_masks(detections, proto) return detections, masks def process_proto_masks(detections, proto): """ 从原型掩码生成实例分割掩码 """ # 提取分割系数 mask_coef = detections[:, 6:6+32] # 假设有32个系数 # 计算掩码 masks = torch.sigmoid(mask_coef @ proto.view(32, -1)).view(-1, *proto.shape[2:]) return masks def visualize_segmentation_results(image_path, detections, masks, class_names): """ 可视化分割结果 """ # 读取原始图像 img = imread(image_path) if img.ndim == 2: # 转换为伪彩色以便可视化 img = np.stack([img, img, img], axis=-1) # 绘制边界框和分割掩码 for i, det in enumerate(detections): *xyxy, conf, cls = det x1, y1, x2, y2 = map(int, xyxy) # 绘制矩形 color = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) # 标签 label = f"{class_names[int(cls)]} {conf:.2f}" cv2.putText(img, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # 绘制分割掩码 if masks is not None and i < len(masks): mask = masks[i] # 创建彩色掩码 mask_img = np.zeros_like(img) mask_img[mask == 1] = color # 融合到原始图像 img = cv2.addWeighted(img, 1, mask_img, 0.5, 0) # 保存结果 output_path = image_path.replace('.tif', '_segmentation.tif') imsave(output_path, img.astype(np.float32)) print(f"Segmentation result saved to {output_path}") # 显示(转换为8位) display_img = (img * 255).astype(np.uint8) cv2.imshow("Segmentation Result", display_img) cv2.waitKey(0) cv2.destroyAllWindows() ``` ## 关键修改点详解 ### 1. YOLOv8YOLOv8-seg的转换 - **模型结构差异**:YOLOv8-seg增加了一个分割头,输出分割原型和系数 - **输入层修改**:同样需要将第一层卷积从3通道改为1通道 - **损失函数**:使用`v8SegmentationLoss`同时处理检测和分割损失 ### 2. 分割数据处理 - **掩码加载**:添加对分割掩码的支持(PNG格式) - **掩码预处理**:调整为分割头输出尺寸(通常为输入的1/4) - **数据增强**:保持图像和掩码的同步增强 ### 3. 分割训练优化 - **批量大小**:分割任务需要更多内存,减小批量大小 - **掩码尺寸**:设置合适的掩码下采样尺寸(通常为160×160) - **损失权重**:平衡检测损失和分割损失的权重 ### 4. 分割推理处理 - **输出处理**:同时处理检测框和分割掩码 - **掩码生成**:使用分割系数和原型生成实例掩码 - **结果融合**:将检测框和分割掩码融合显示 ## YOLOv8-seg专用修改 ### 1. 分割头适配 ```python # 在模型修改函数中添加分割头检查 def adapt_yolov8seg_for_single_channel(model_path): # ... [之前代码] # 检查分割头是否正常 if not hasattr(model, 'proto') or not hasattr(model, 'seg'): print("Warning: Model segmentation head may not be properly configured") # 确保分割头权重不变 print("Segmentation head parameters remain unchanged") return model ``` ### 2. 分割损失优化 ```python # 自定义分割损失权重 class CustomSegmentationLoss(v8SegmentationLoss): def __init__(self, model, seg_weight=1.5): super().__init__(model) self.seg_weight = seg_weight # 增加分割损失的权重 def __call__(self, preds, targets, seg_preds, masks): loss, loss_items = super().__call__(preds, targets, seg_preds, masks) # 调整分割损失权重 loss_items['seg_loss'] *= self.seg_weight loss = loss_items['det_loss'] + loss_items['seg_loss'] return loss, loss_items ``` ### 3. 掩码后处理优化 ```python def refine_segmentation_masks(masks, detections, min_area=100): """ 精炼分割掩码:移除小区域,填充空洞 """ refined_masks = [] for i in range(len(masks)): mask = masks[i].astype(np.uint8) # 移除小区域 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: if cv2.contourArea(cnt) < min_area: cv2.drawContours(mask, [cnt], 0, 0, -1) # 填充空洞 kernel = np.ones((5,5), np.uint8) mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) refined_masks.append(mask) return np.array(refined_masks) ``` ### 4. 多实例掩码处理 ```python def combine_instance_masks(masks): """ 将多个实例掩码组合成单一标签图像 """ if len(masks) == 0: return np.zeros_like(masks[0]) combined = np.zeros_like(masks[0], dtype=np.uint16) for i, mask in enumerate(masks): combined[mask > 0] = i + 1 # 实例ID从1开始 return combined ``` ## 完整工作流程 1. **准备数据**: - 组织TIFF图像 - YOLO格式检测标注 - PNG格式分割掩码 2. **修改模型**: ```python adapted_model = adapt_yolov8seg_for_single_channel('yolov8n-seg.pt') torch.save(adapted_model.state_dict(), 'yolov8n-seg_single_channel.pt') ``` 3. **训练模型**: ```python trained_model = train_yolov8seg_tiff() ``` 4. **推理测试**: ```python detections, masks = predict_segmentation_on_tiff(trained_model, 'test.tif') ``` 5. **结果可视化**: ```python visualize_segmentation_results('test.tif', detections, masks, class_names=['object']) ``` 6. **保存结果**: ```python # 保存组合掩码 combined_mask = combine_instance_masks(masks) imsave('test_mask.tif', combined_mask) ``` ## 性能优化技巧 1. **混合精度训练**: ```python scaler = torch.cuda.amp.GradScaler() with torch.cuda.amp.autocast(): preds, seg_preds = model(imgs) loss, loss_items = loss_fn(preds, labels, seg_preds, masks) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() ``` 2. **掩码压缩存储**: ```python # 使用LZW压缩保存TIFF掩码 imsave('mask.tif', combined_mask, compression='lzw') ``` 3. **批处理推理**: ```python def batch_predict_segmentation(model, image_paths, batch_size=4): """批处理推理提高效率""" # 预处理所有图像 # ... # 批处理推理 results = [] for i in range(0, len(images), batch_size): batch = images[i:i+batch_size] batch_tensor = torch.stack(batch).to(device) with torch.no_grad(): preds, proto = model(batch_tensor) # 处理每个图像的输出 for j in range(len(batch)): detections, masks = process_segmentation_output( preds[j:j+1], proto[j:j+1], conf_thres, iou_thres ) results.append((detections, masks)) return results ```
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