diff --git a/vis4d/zoo/qdtrack/qdtrack_yolox_x_50e_bdd100k.py b/vis4d/zoo/qdtrack/qdtrack_yolox_x_50e_bdd100k.py
deleted file mode 100644
index 60454d36..00000000
--- a/vis4d/zoo/qdtrack/qdtrack_yolox_x_50e_bdd100k.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# pylint: disable=duplicate-code
-"""QDTrack with YOLOX-x on BDD100K."""
-from __future__ import annotations
-
-import pytorch_lightning as pl
-from lightning.pytorch.callbacks import ModelCheckpoint
-
-from vis4d.config import class_config
-from vis4d.config.typing import ExperimentConfig, ExperimentParameters
-from vis4d.data.datasets.bdd100k import bdd100k_track_map
-from vis4d.data.io.hdf5 import HDF5Backend
-from vis4d.engine.callbacks import EvaluatorCallback, VisualizerCallback
-from vis4d.engine.connectors import CallbackConnector, DataConnector
-from vis4d.eval.bdd100k import BDD100KTrackEvaluator
-from vis4d.vis.image import BoundingBoxVisualizer
-from vis4d.zoo.base import (
-    get_default_callbacks_cfg,
-    get_default_cfg,
-    get_default_pl_trainer_cfg,
-)
-from vis4d.zoo.base.data_connectors import CONN_BBOX_2D_TRACK_VIS
-from vis4d.zoo.base.datasets.bdd100k import CONN_BDD100K_TRACK_EVAL
-from vis4d.zoo.base.models.qdtrack import (
-    CONN_BBOX_2D_TEST,
-    CONN_BBOX_2D_TRAIN,
-    get_qdtrack_yolox_cfg,
-)
-from vis4d.zoo.base.models.yolox import (
-    get_yolox_callbacks_cfg,
-    get_yolox_optimizers_cfg,
-)
-from vis4d.zoo.qdtrack.data_yolox import get_bdd100k_track_cfg
-
-
-def get_config() -> ExperimentConfig:
-    """Returns the config dict for qdtrack on bdd100k.
-
-    Returns:
-        ExperimentConfig: The configuration
-    """
-    ######################################################
-    ##                    General Config                ##
-    ######################################################
-    config = get_default_cfg(exp_name="qdtrack_yolox_x_50e_bdd100k")
-    config.checkpoint_period = 5
-    config.check_val_every_n_epoch = 5
-
-    # Hyper Parameters
-    params = ExperimentParameters()
-    params.samples_per_gpu = 8  # batch size = 8 GPUs * 8 samples per GPU = 64
-    params.workers_per_gpu = 8
-    params.lr = 0.001
-    params.num_epochs = 25
-    config.params = params
-
-    ######################################################
-    ##          Datasets with augmentations             ##
-    ######################################################
-    data_backend = class_config(HDF5Backend)
-
-    config.data = get_bdd100k_track_cfg(
-        data_backend=data_backend,
-        samples_per_gpu=params.samples_per_gpu,
-        workers_per_gpu=params.workers_per_gpu,
-    )
-
-    ######################################################
-    ##                        MODEL                     ##
-    ######################################################
-    num_classes = len(bdd100k_track_map)
-    weights = (
-        "mmdet://yolox/yolox_x_8x8_300e_coco/"
-        "yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth"
-    )
-    config.model, config.loss = get_qdtrack_yolox_cfg(
-        num_classes, "xlarge", weights=weights
-    )
-
-    ######################################################
-    ##                    OPTIMIZERS                    ##
-    ######################################################
-    # we use a schedule with 50 epochs, but only train for 25 epochs
-    num_total_epochs, num_last_epochs = 50, 10
-    config.optimizers = get_yolox_optimizers_cfg(
-        params.lr, num_total_epochs, 1, num_last_epochs
-    )
-
-    ######################################################
-    ##                  DATA CONNECTOR                  ##
-    ######################################################
-    config.train_data_connector = class_config(
-        DataConnector, key_mapping=CONN_BBOX_2D_TRAIN
-    )
-
-    config.test_data_connector = class_config(
-        DataConnector, key_mapping=CONN_BBOX_2D_TEST
-    )
-
-    ######################################################
-    ##                     CALLBACKS                    ##
-    ######################################################
-    # Logger and Checkpoint
-    callbacks = get_default_callbacks_cfg(
-        config.output_dir, refresh_rate=config.log_every_n_steps
-    )
-
-    # YOLOX callbacks
-    callbacks += get_yolox_callbacks_cfg(
-        switch_epoch=num_total_epochs - num_last_epochs, num_sizes=0
-    )
-
-    # Visualizer
-    callbacks.append(
-        class_config(
-            VisualizerCallback,
-            visualizer=class_config(
-                BoundingBoxVisualizer, vis_freq=500, image_mode="BGR"
-            ),
-            save_prefix=config.output_dir,
-            test_connector=class_config(
-                CallbackConnector, key_mapping=CONN_BBOX_2D_TRACK_VIS
-            ),
-        )
-    )
-
-    # Evaluator
-    callbacks.append(
-        class_config(
-            EvaluatorCallback,
-            evaluator=class_config(
-                BDD100KTrackEvaluator,
-                annotation_path="data/bdd100k/labels/box_track_20/val/",
-            ),
-            test_connector=class_config(
-                CallbackConnector, key_mapping=CONN_BDD100K_TRACK_EVAL
-            ),
-        )
-    )
-
-    config.callbacks = callbacks
-
-    ######################################################
-    ##                     PL CLI                       ##
-    ######################################################
-    # PL Trainer args
-    pl_trainer = get_default_pl_trainer_cfg(config)
-    pl_trainer.max_epochs = params.num_epochs
-    pl_trainer.check_val_every_n_epoch = config.check_val_every_n_epoch
-    pl_trainer.checkpoint_callback = class_config(
-        ModelCheckpoint,
-        dirpath=config.get_ref("output_dir") + "/checkpoints",
-        verbose=True,
-        save_last=True,
-        save_on_train_epoch_end=True,
-        every_n_epochs=config.checkpoint_period,
-        save_top_k=5,
-        mode="max",
-        monitor="step",
-    )
-    pl_trainer.wandb = True
-    pl_trainer.precision = "16-mixed"
-    config.pl_trainer = pl_trainer
-
-    # PL Callbacks
-    pl_callbacks: list[pl.callbacks.Callback] = []
-    config.pl_callbacks = pl_callbacks
-
-    return config.value_mode()