MMDetWandbHook that can be used to:wandb libraryMMDetWandbHookMMDetWandbHook to the MMDetection log_config method of the config system.init_kwargsdict) A dict passed to wandb.init to initialize a W&B run.intervalint) Logging interval (every k iterations). Defaults to 50.log_checkpointbool) Save the checkpoint at every checkpoint interval as W&B Artifacts. Use this for model versioning where each version is a checkpoint. Defaults to False.log_checkpoint_metadatabool) Log the evaluation metrics computed on the validation data with the checkpoint, along with current epoch as a metadata to that checkpoint. Defaults to True.num_eval_imagesint) The number of validation images to be logged. If zero, the evaluation won't be logged. Defaults to 100.bbox_score_thrfloat) Threshold for bounding box scores. Defaults to 0.3.init_kwargs argument in MMDetWandbHook. This argument takes in a dictionary of key-value pairs which in turn is passed to wandb.init which controls which project your run is logged to as well as other features of your run.log_checkpoint=True argument in MMDetWandbHook. This feature depends on the MMCV's CheckpointHook that periodically save the model checkpoints. The period is determined by checkpoint_config.interval.log_checkpoint_metadata is True, every checkpoint version will have metadata associated with it. This feature depends on the CheckpointHook as well as EvalHook or DistEvalHook. The metadata is logged only when the checkpoint interval is divisible by evaluation interval.MMDetWandbHook you can now log the validation data as W&B Tables and create versioned W&B Tables for model prediction.num_eval_images argument controls the number of validation samples that are logged as W&B Tables. Here are a few things to note:num_eval_images=0 the validation data, as well as model predictions, will not be logged.validate=False for mmdet.core.train_detector API, the validation data and model predictions will not be logged.num_eval_images is greater than the total number of validation samples, the complete validation dataset is logged.val_data is uploaded only once. The run_<id>_predtable and subsequent runs use referencing to the uploaded data to save memory. A new version of val_data is created only when it's changed.