关于自己的数据训练

我现在准备了一份自己的数据集，标准的COCO格式。并且也准备了一份转化的YOLO格式的数据集。对这份数据也完成了切分，切分后的格式也准备成了YOLO格式，格式如下：如果我想使用该仓库进行半监督的任务，请问我应该如何操作？

您好，将train2017.1@1.txt填入Dataset下的train，将train2017.1@1-unlabeled.txt填入Dataset下的target，以此类推

您好，将train2017.1@1.txt填入Dataset下的train，将train2017.1@1-unlabeled.txt填入Dataset下的target，以此类推

感谢！还有一些问题向您请教，紧接着我根据readme.md文件中，直接进行半监督训练，进行了如下操作： 1.在配置文件configs/ssod/custom/yolov5l_custom_ssod.yaml中加载了事先在yolov5上训练好的权重，并修改了如下内容： weights: '/home/cv/yy/5.semi-object/efficientteacher-main/weights/train_yolov5/best.pt'
Model: depth_multiple: 0.33 # 1.00 # model depth multiple width_multiple: 0.50 # 1.00 # layer channel multiple Dataset: data_name: 'coco' train: /home/cv/yy/5.semi-object/efficientteacher-main/datasets/coco/semi/train2017.1@50.txt # data/custom_train.txt # 118287 images val: /home/cv/yy/5.semi-object/efficientteacher-main/datasets/coco/val2017.txt # data/custom_val.txt # 5000 images test: /home/cv/yy/5.semi-object/efficientteacher-main/datasets/coco/val2017.txt # data/custom_val.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794^ target: /home/cv/yy/5.semi-object/efficientteacher-main/datasets/coco/semi/train2017.1@50-unlabeled.txt nc: 7 # 2 # number of classes np: 0 #number of keypoints names: ['gram', 'pseudogram', 'moni', 'glyc', 'gloeo', 'clavi', 'anth'] img_size: 640 batch_size: 16 2.然后开始训练，遇到如下问题：

EfficientTeacher  2023-3-2 torch 1.11.0 CUDA:0 (NVIDIA GeForce RTX 3090, 24245.8125MB)

Weights & Biases: run "pip install wandb" to automatically track and visualize EfficientTeacher runs (RECOMMENDED)
TensorBoard: Start with "tensorboard --logdir yolov5_ssod", view at http://localhost:6006/
Traceback (most recent call last):
  File "train.py", line 84, in <module>
    main(opt)
  File "train.py", line 72, in main
    trainer = SSODTrainer(cfg, device, callbacks, LOCAL_RANK, RANK, WORLD_SIZE)
  File "/home/cv/yy/5.semi-object/efficientteacher-main/trainer/ssod_trainer.py", line 58, in __init__
    self.build_model(cfg, device)
  File "/home/cv/yy/5.semi-object/efficientteacher-main/trainer/ssod_trainer.py", line 103, in build_model
    ckpt = torch.load(weights, map_location=device)  # load checkpoint
  File "/home/cv/.conda/envs/yolox/lib/python3.8/site-packages/torch/serialization.py", line 712, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/home/cv/.conda/envs/yolox/lib/python3.8/site-packages/torch/serialization.py", line 1046, in _load
    result = unpickler.load()
  File "/home/cv/.conda/envs/yolox/lib/python3.8/site-packages/torch/serialization.py", line 1039, in find_class
    return super().find_class(mod_name, name)
ModuleNotFoundError: No module named "models.yolo"

为什么会找不到呢，将其修改为from scripts.mula_convertor.models.yolo import Detect, Model依然出现该问题？运行python train.py是在efficientteacher-main项目目录下：/home/cv/yy/5.semi-object/efficientteacher-main (2)是不是应该首先通过convert_pt_to_efficient.py进行转化呢？使用该脚本，遇到错误

Traceback (most recent call last):
  File "scripts/mula_convertor/convert_pt_to_efficient.py", line 92, in <module>
    convert_yolov5_to_efficient( '/home/cv/yj/5.semi-object/efficientteacher-main/weights/train_yolov5/best.pt', '/home/cv/yj/5.semi-object/efficientteacher-main/configs/ssod/custom/yolov5s_spore_ssod.yaml','/home/cv/yj/5.semi-object/efficientteacher-main/efficient-yolov5s.pt')
  File "scripts/mula_convertor/convert_pt_to_efficient.py", line 16, in convert_yolov5_to_efficient
    with open(map_path, 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'map.txt'

请问map.txt是什么文件

3.readme文件中说默认的半监督训练方案是先训220epoch的有监督，然后进入半监督。配置文件configs/ssod/custom/yolov5l_custom_ssod.yaml中epoch设置的是20，请问这个20起作用了吗 @BowieHsu

@yjcreation 1.您好，根据log, 问题出在yaml里的这一行 weights: '/home/cv/yy/5.semi-object/efficientteacher-main/weights/train_yolov5/best.pt'，您这个pt应该是用原生YOLOv5代码训练出来的，请使用我们提供的转换脚本转成我们的格式，再加载就不会报这个错 2.转换脚本下面的map是用来替换state_dict中的模块名，因为原生YOLOv5代码是使用yaml来定义网络结构，而我们的代码是使用backbone/neck/head来定义网络结构的，所以需要使用一个脚本来替换state_dict中的前缀，这个过程不会对权重进行修改，只是换掉了索引名 3.custom中的epoch 20的意思是总共进行20轮训练，而有监督轮次的控制参数为burn_in_epoch，这里设置多少就会先进行多少次有监督训练，然后进行(epoch - burn_in_epoch)次的半监督训练

FileNotFoundError: [Errno 2] No such file or directory: 'map.txt'

非常感谢！您好！针对问题2，FileNotFoundError: [Errno 2] No such file or directory: 'map.txt' 必须在这个目录下运行吗？在指定cfg_path参数的时候必须指定yolov5l_custom.yaml配置文件（该配置文件中没有半监督的参数），而不能直接指定yolov5l_custom_ssod.yaml配置文件吗？如果指定yolov5l_custom_ssod.yaml配置文件，则会出现如下错误：

  File "/home/cv/.conda/envs/yolox/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/cv/.conda/envs/yolox/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
    cli.main()
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
    run()
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
    runpy.run_path(target, run_name="__main__")
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/cv/.vscode-server/extensions/ms-python.python-2023.4.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
    exec(code, run_globals)
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/convert_pt_to_efficient.py", line 92, in <module>
    convert_yolov5_to_efficient( '/home/cv/yj/5.semi-object/efficientteacher-main/weights/train_yolov5/best.pt', '/home/cv/yy/5.semi-object/efficientteacher-main/configs/ssod/custom/yolov5s_spore_ssod.yaml','/home/cv/yj/5.semi-object/efficientteacher-main/efficient-yolov5s.pt')
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/convert_pt_to_efficient.py", line 40, in convert_yolov5_to_efficient
    cfg.merge_from_file(cfg_path)
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/configs/yacs.py", line 209, in merge_from_file
    self.merge_from_other_cfg(cfg)
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/configs/yacs.py", line 213, in merge_from_other_cfg
    _merge_a_into_b(cfg_other, self, self, [])
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/configs/yacs.py", line 460, in _merge_a_into_b
    _merge_a_into_b(v, b[k], root, key_list + [k])
  File "/home/cv/yy/5.semi-object/efficientteacher-main/scripts/mula_convertor/configs/yacs.py", line 473, in _merge_a_into_b
    raise KeyError("Non-existent config key: {}".format(full_key))
KeyError: 'Non-existent config key: SSOD.ignore_thres_low'

指定yolov5l_custom.yaml配置文件，则成功转换了模型 @BowieHsu

@BowieHsu _and the control parameter of the supervised round is burn_in_epoch. As many times as set here, the number of times of supervised training will be performed first, and then (epoch - burn_in_epoch) times semi-supervised training of_

What would be labels used here for 200 epochs during in supervised phase, is it referring to the 10% labelled images label ? What are the pretrained weights ,are they the native YOlo L weights on coco set which we convert first ?

AlibabaResearch / efficientteacher

关于自己的数据训练 #14