Learn about Configs

We use python files as our config system. You can find all the provided configs under $MMDetection/configs.

We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments. If you wish to inspect the config file, you may run python tools/misc/print_config.py /PATH/TO/CONFIG to see the complete config.

A brief description of a complete config

A complete config usually contains the following primary fields:

• model: the basic config of model, which may contain data_preprocessor, modules (e.g., detector, motion),train_cfg, test_cfg, etc.

• train_dataloader: the config of training dataloader, which usually contains batch_size, num_workers, sampler, dataset, etc.

• val_dataloader: the config of validation dataloader, which is similar with train_dataloader.

• test_dataloader: the config of testing dataloader, which is similar with train_dataloader.

• val_evaluator: the config of validation evaluator. For example,type='MOTChallengeMetrics' for MOT task on the MOTChallenge benchmarks.

• test_evaluator: the config of testing evaluator, which is similar with val_evaluator.

• train_cfg: the config of training loop. For example, type='EpochBasedTrainLoop'.

• val_cfg: the config of validation loop. For example, type='VideoValLoop'.

• test_cfg: the config of testing loop. For example, type='VideoTestLoop'.

• default_hooks: the config of default hooks, which may include hooks for timer, logger, param_scheduler, checkpoint, sampler_seed, visualization, etc.

• vis_backends: the config of visualization backends, which uses type='LocalVisBackend' as default.

• visualizer: the config of visualizer. type='TrackLocalVisualizer' for MOT tasks.

• param_scheduler: the config of parameter scheduler, which usually sets the learning rate scheduler.

• optim_wrapper: the config of optimizer wrapper, which contains optimization-related information, for example optimizer, gradient clipping, etc.

• load_from: load models as a pre-trained model from a given path.

• resume: If True, resume checkpoints from load_from, and the training will be resumed from the epoch when the checkpoint is saved.

Modify config through script arguments

When submitting jobs using tools/train.py or tools/test_tracking.py, you may specify --cfg-options to in-place modify the config. We present several examples as follows. For more details, please refer to MMEngine.

• Update config keys of dict chains.

  The config options can be specified following the order of the dict keys in the original config. For example, --cfg-options model.detector.backbone.norm_eval=False changes the all BN modules in model backbones to train mode.

• Update keys inside a list of configs.

  Some config dicts are composed as a list in your config. For example, the testing pipeline test_dataloader.dataset.pipeline is normally a list e.g. [dict(type='LoadImageFromFile'), ...]. If you want to change LoadImageFromFile to LoadImageFromWebcam in the pipeline, you may specify --cfg-options test_dataloader.dataset.pipeline.0.type=LoadImageFromWebcam.

• Update values of list/tuples.

  Maybe the value to be updated is a list or a tuple. For example, you can change the key mean of data_preprocessor by specifying --cfg-options model.data_preprocessor.mean=[0,0,0]. Note that NO white space is allowed inside the specified value.

Config File Structure

There are 3 basic component types under config/_base_, i.e., dataset, model and default_runtime. Many methods could be easily constructed with one of each like SORT, DeepSORT. The configs that are composed by components from _base_ are called primitive.

For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.

For easy understanding, we recommend contributors to inherit from exiting methods. For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying _base_ = ../_base_/models/faster-rcnn_r50-dc5.py, then modify the necessary fields in the config files.

If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder method_name under configs.

Please refer to MMEngine for detailed documentation.

Config Name Style

We follow the below style to name config files. Contributors are advised to follow the same style.

{method}_{module}_{train_cfg}_{train_data}_{test_data}

• {method}: method name, like sort.

• {module}: basic modules of the method, like faster-rcnn_r50_fpn.

• {train_cfg}: training config which usually contains batch size, epochs, etc, like 8xb4-80e.

• {train_data}: training data, like mot17halftrain.

• {test_data}: testing data, like test-mot17halfval.

FAQ

Ignore some fields in the base configs

Sometimes, you may set _delete_=True to ignore some of fields in base configs. You may refer to MMEngine for simple illustration.

Tracking Data Structure Introduction

Advantages and new features

In mmdetection tracking task, we employ videos to organize the dataset and use TrackDataSample to descirbe dataset info.

• Based on video organization, we provide transform UniformRefFrameSample to sample key frames and ref frames and use TransformBroadcaster for for clip training.

• TrackDataSample can be viewd as a wrapper of multiple DetDataSample to some extent. It contains a property video_data_samples which is a list of DetDataSample, each of which corresponds to a single frame. In addition, it’s metainfo includes key_frames_inds and ref_frames_inds to apply clip training way.

• Thanks to video-based data organization, the entire video can be directly tested. This way is more concise and intuitive. We also provide image_based test method, if your GPU mmemory cannot fit the entire video.

TODO

• Some algorithms like StrongSORT, Mask2Former can not support video_based testing. These algorithms pose a challenge to GPU memory. we will optimize this problem in the future.

• Now we do not support joint training of video_based dataset like MOT Challenge Dataset and image_based dataset like Crowdhuman for the algorithm QDTrack. we will optimize this problem in the future.