yolox person detection result can not reproduce

[alexsun009]

Hi, thanks for the work:
for MOT20 detection model, I try same dataset (train: crowdhuman+MOT20 train, test: MOT20 test), but the yolox detection model person detection result on MOT is much lower than your model: my one only around 60%mAP compare to you release yolox model 82% mAP in person detection,
could help take a look the parameter that I use which might go wrong, thanks

parameters that I use:

class Exp(BaseExp):
def init(self):
super().init()

    # ---------------- model config ---------------- #
    # detect classes number of model
    self.num_classes = 1
    # factor of model depth
    self.depth = 1.00
    # factor of model width
    self.width = 1.00
    # activation name. For example, if using "relu", then "silu" will be replaced to "relu".
    self.act = "silu"

    # ---------------- dataloader config ---------------- #
    # set worker to 4 for shorter dataloader init time
    # If your training process cost many memory, reduce this value.
    self.data_num_workers = 4
    self.input_size = (800, 1440)  # (height, width)
    # Actual multiscale ranges: [640 - 5 * 32, 640 + 5 * 32].
    # To disable multiscale training, set the value to 0.
    self.multiscale_range = 7
    # You can uncomment this line to specify a multiscale range
    # self.random_size = (14, 26)
    # dir of dataset images, if data_dir is None, this project will use `datasets` dir
    self.data_dir = None
    # name of annotation file for training
    self.train_ann = "mot20train_CHtrain_1classonlyv2.json"

    #self.train_ann = "mot20_test_1classonly.json"
    #self.train_ann = "mot20_test.json"
    # name of annotation file for evaluation
    self.val_ann = "mot20_test_1classonly.json"
    # name of annotation file for testing
    self.test_ann = "mot20_test_1classonly.json"

    # --------------- transform config ----------------- #
    # prob of applying mosaic aug
    self.mosaic_prob = 1.0
    # prob of applying mixup aug
    self.mixup_prob = 1.0
    # prob of applying hsv aug
    self.hsv_prob = 1.0
    # prob of applying flip aug
    self.flip_prob = 0.5
    # rotation angle range, for example, if set to 2, the true range is (-2, 2)
    self.degrees = 10.0
    # translate range, for example, if set to 0.1, the true range is (-0.1, 0.1)
    self.translate = 0.1
    self.mosaic_scale = (0.1, 2)
    # apply mixup aug or not
    self.enable_mixup = True
    self.mixup_scale = (0.5, 1.5)
    # shear angle range, for example, if set to 2, the true range is (-2, 2)
    self.shear = 2.0

    # --------------  training config --------------------- #
    # epoch number used for warmup
    self.warmup_epochs = 1
    # max training epoch
    self.max_epoch = 300
    # minimum learning rate during warmup
    self.warmup_lr = 0
    self.min_lr_ratio = 0.05
    # learning rate for one image. During training, lr will multiply batchsize.
    self.basic_lr_per_img = 0.000001 / 64.0
    # name of LRScheduler
    self.scheduler = "yoloxwarmcos"
    # last #epoch to close augmention like mosaic
    self.no_aug_epochs = 1
    # apply EMA during training
    self.ema = True

    # weight decay of optimizer
    self.weight_decay = 5e-4
    # momentum of optimizer
    self.momentum = 0.9
    # log period in iter, for example,
    # if set to 1, user could see log every iteration.
    self.print_interval = 10
    # eval period in epoch, for example,
    # if set to 1, model will be evaluate after every epoch.
    self.eval_interval = 1
    # save history checkpoint or not.
    # If set to False, yolox will only save latest and best ckpt.
    self.save_history_ckpt = False
    # name of experiment
    self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

    # -----------------  testing config ------------------ #
    # output image size during evaluation/test
    self.test_size = (800, 1440)
    # confidence threshold during evaluation/test,
    # boxes whose scores are less than test_conf will be filtered
    self.test_conf = 0.01
    # nms threshold
    self.nmsthre = 0.65

[alexsun009]

know the learning rate I use is very low, but use same learning rate e-3 suggested in paper for yolox detection, model will overfit in first epoch.
this is another thing that I am confused, thanks
thanks