fast r-cnn训练有丝分裂数据集python代码

以下是使用 PyTorch 实现 Fast R-CNN 在有丝分裂数据集上进行训练的代码：

import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.datasets import CocoDetection
from torchvision.transforms import ToTensor, Normalize, Compose
from torch.utils.data import DataLoader
from engine import train_one_epoch, evaluate
import utils

# define dataset and data loader
dataset = CocoDetection(root='path/to/your/coco/dataset', annFile='path/to/your/coco/annotations', transforms=Compose([ToTensor(), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))
data_loader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4, collate_fn=utils.collate_fn)

# define model
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = 2  # 1 class (mitotic figure) + background
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

# define optimizer and learning rate scheduler
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)

# train model for 10 epochs
num_epochs = 10
for epoch in range(num_epochs):
    train_one_epoch(model, optimizer, data_loader, torch.device('cuda'))
    lr_scheduler.step()
    evaluate(model, data_loader, torch.device('cuda'))

其中，`engine.py` 和 `utils.py` 文件可以从 PyTorch 官方 GitHub 仓库中的 `references/detection` 文件夹中获取。在此代码中，我们使用了预训练的 Faster R-CNN 模型，并将其修改为只预测有丝分裂图像中存在或不存在细胞有丝分裂的二元分类问题。训练数据集需要满足 COCO 数据集的格式。