Yolox_nano训练优化

1 原始图片的分辨率对Yolox_nano的训练结果有何影响？640 × 480合适还是别的H × W合适？
2 默认的yolox_base.py中的参数需要如何调整？比如数据增强的enable_mixup等变量设置；

    # ---------------- dataloader config ---------------- #
    # set worker to 4 for shorter dataloader init time
    self.data_num_workers = 4
    self.input_size = (640, 640)  # (height, width)
    # Actual multiscale ranges: [640-5*32, 640+5*32].
    # To disable multiscale training, set the
    # self.multiscale_range to 0.
    self.multiscale_range = 5
    # You can uncomment this line to specify a multiscale range
    # self.random_size = (14, 26)
    self.data_dir = None
    self.train_ann = "instances_train2017.json"
    self.val_ann = "instances_val2017.json"

    # --------------- transform config ----------------- #
    self.mosaic_prob = 1.0
    self.mixup_prob = 1.0
    self.hsv_prob = 1.0
    self.flip_prob = 0.5
    self.degrees = 10.0
    self.translate = 0.1
    self.mosaic_scale = (0.1, 2)
    self.mixup_scale = (0.5, 1.5)
    self.shear = 2.0
    self.enable_mixup = True

    # --------------  training config --------------------- #
    self.warmup_epochs = 5
    self.max_epoch = 300
    self.warmup_lr = 0
    self.basic_lr_per_img = 0.01 / 64.0
    self.scheduler = "yoloxwarmcos"
    self.no_aug_epochs = 15
    self.min_lr_ratio = 0.05
    self.ema = True

    self.weight_decay = 5e-4
    self.momentum = 0.9
    self.print_interval = 10
    self.eval_interval = 10
    self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

    # -----------------  testing config ------------------ #
    self.test_size = (640, 640)
    self.test_conf = 0.01
    self.nmsthre = 0.65

Start with the yolox_base.py configuration and review the listed dataloader, transform, training, and testing settings. Establish comparisons for input resolution and augmentation choices, then report training results for the Yolox_nano model. The issue names no test, benchmark, or specific configuration entry point beyond yolox_base.py.