model=build_detector(cfg.model,...)#6.初始化 datasets #===================mmdet/apis/train.py==================#1.初始化 data_loaders ,内部会初始化 GroupSampler data_loader=DataLoader(dataset,...)#2.基于是否使用分布式训练,初始化对
config=mmcv.Config.fromfile(config)# Set pretrained to be None since wedonot need pretrained model here config.model.pretrained=None # Initialize the detector model=build_detector(config.model)# Load checkpoint checkpoint=load_checkpoint(model,checkpoint,map_location=device)# Set the classesofmodelsfo...
log_level=cfg.log_level)# 4.收集运行环境并且打印,方便排查硬件和软件相关问题env_info_dict=collect_env()# 5.初始化 modelmodel=build_detector(cfg.model,...)# 6.初始
model = init_detector(config_file,checkpoint_file) img_dir = 'data/VOCdevkit/VOC2007/JPEGImages/' out_dir = 'results/' if not os.path.exists(out_dir): os.mkdir(out_dir) # img = 'test.jpg' # result = inference_detector(model,img) # show_result(img, result, model.CLASSES, out_...
show_result(img, result, model.CLASSES) 六、总结 本文介绍了MMDetection的安装、配置和使用方法,包括环境搭建、安装MMDetection、配置MMDetection和使用MMDetection的命令行接口和Python接口。通过本文的学习,读者可以快速上手MMDetection,并利用其强大的功能完成目标检测任务。同时,读者也可以根据自己的需要,对MMDetection进行...
model=build_detector(config.model) # Load checkpoint checkpoint=load_checkpoint(model,checkpoint,map_location=device) # Set the classes of models for inference model.CLASSES=checkpoint['meta']['CLASSES'] # We need to set the model's cfg for inference ...
model = init_detector(config_file, checkpoint_file, device='cuda:0') image = mmcv.imread('demo/demo.jpg',channel_order='rgb')result = inference_detector(model, image) from mmdet.registryimportVISUALIZERSvisualizer = VISUALIZERS.build(model.cfg...
MMdetection之build_optimizer模块解读 前言 前面文章build_dataset,build_dataloader,build_model均以做了详细的介绍,而optimizer作为“炼丹”的最后一个条件,本文将介绍mmdetection是如何构建优化器的。 1、总体流程 总体流程和构建model过程类似。首先mmdetection建立了一个优化器注册器,里面注册了DefaultOptimizer...
import mmcvfrom mmcv.runner import load_checkpointfrom mmdet.models import build_detectorfrom mmdet.apis import inference_detector, show_resultcfg = mmcv.Config.fromfile('configs/faster_rcnn_r50_fpn_1x.py')cfg.model.pretrained = None# construct the model and load checkpointmodel = build_detector...
runner = _build_demo_runner() runner.model = demo_model # 设置了HIGHREST的优先级 runner.register_hook(resume_ema_hook, priority='HIGHEST') checkpointhook = CheckpointHook(interval=1, by_epoch=True) runner.register_hook(checkpointhook)