train YOLOv8-S with 300 epoch

yolo/config/gelan_config.py

@@ -100,7 +100,7 @@ class GElanBaseConfig(object):
         # ---------------- Lr Scheduler config ----------------
         self.warmup_epoch = 3
         self.lr_scheduler = "cosine"
-        self.max_epoch    = 500
+        self.max_epoch    = 300
         self.eval_epoch   = 10
         self.no_aug_epoch = 20
 

yolo/config/yolov7_af_config.py

@@ -1,151 +0,0 @@
-# yolo Config
-
-
-def build_yolov7af_config(args):
-    if   args.model == 'yolov7_af_t':
-        return Yolov7AFTConfig()
-    elif args.model == 'yolov7_af_l':
-        return Yolov7AFLConfig()
-    else:
-        raise NotImplementedError("No config for model: {}".format(args.model))
-    
-# YOLOv7AF-Base config
-class Yolov7AFBaseConfig(object):
-    def __init__(self) -> None:
-        # ---------------- Model config ----------------
-        self.width    = 1.0
-        self.out_stride = [8, 16, 32]
-        self.max_stride = 32
-        self.num_levels = 3
-        self.scale      = "b"
-        ## Backbone
-        self.bk_act   = 'silu'
-        self.bk_norm  = 'BN'
-        self.bk_depthwise = False
-        self.use_pretrained = True
-        ## Neck
-        self.neck_act       = 'silu'
-        self.neck_norm      = 'BN'
-        self.neck_depthwise = False
-        self.neck_expand_ratio = 0.5
-        self.spp_pooling_size  = 5
-        ## FPN
-        self.fpn_act  = 'silu'
-        self.fpn_norm = 'BN'
-        self.fpn_depthwise = False
-        self.fpn_expansions = [0.5, 0.5]
-        self.fpn_block_bw = 4
-        self.fpn_block_dw = 1
-        ## Head
-        self.head_act  = 'silu'
-        self.head_norm = 'BN'
-        self.head_depthwise = False
-        self.head_dim       = 256
-        self.num_cls_head   = 2
-        self.num_reg_head   = 2
-
-        # ---------------- Post-process config ----------------
-        ## Post process
-        self.val_topk = 1000
-        self.val_conf_thresh = 0.001
-        self.val_nms_thresh  = 0.7
-        self.test_topk = 100
-        self.test_conf_thresh = 0.4
-        self.test_nms_thresh  = 0.5
-
-        # ---------------- Assignment config ----------------
-        ## Matcher
-        self.ota_center_sampling_radius = 2.5
-        self.ota_topk_candidate = 10
-        ## Loss weight
-        self.loss_obj = 1.0
-        self.loss_cls = 1.0
-        self.loss_box = 5.0
-
-        # ---------------- ModelEMA config ----------------
-        self.use_ema = True
-        self.ema_decay = 0.9998
-        self.ema_tau   = 2000
-
-        # ---------------- Optimizer config ----------------
-        self.trainer      = 'yolo'
-        self.optimizer    = 'adamw'
-        self.per_image_lr = 0.001 / 64
-        self.base_lr      = None      # base_lr = per_image_lr * batch_size
-        self.min_lr_ratio = 0.01      # min_lr  = base_lr * min_lr_ratio
-        self.momentum     = 0.9
-        self.weight_decay = 0.05
-        self.clip_max_norm   = 35.0
-        self.warmup_bias_lr  = 0.1
-        self.warmup_momentum = 0.8
-
-        # ---------------- Lr Scheduler config ----------------
-        self.warmup_epoch = 3
-        self.lr_scheduler = "cosine"
-        self.max_epoch    = 300
-        self.eval_epoch   = 10
-        self.no_aug_epoch = 20
-
-        # ---------------- Data process config ----------------
-        self.aug_type = 'yolo'
-        self.box_format = 'xyxy'
-        self.normalize_coords = False
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.0
-        self.copy_paste  = 0.0           # approximated by the YOLOX's mixup
-        self.multi_scale = [0.5, 1.25]   # multi scale: [img_size * 0.5, img_size * 1.25]
-        ## Pixel mean & std
-        self.pixel_mean = [0., 0., 0.]
-        self.pixel_std  = [255., 255., 255.]
-        ## Transforms
-        self.train_img_size = 640
-        self.test_img_size  = 640
-        self.use_ablu = True
-        self.affine_params = {
-            'degrees': 0.0,
-            'translate': 0.2,
-            'scale': [0.1, 2.0],
-            'shear': 0.0,
-            'perspective': 0.0,
-            'hsv_h': 0.015,
-            'hsv_s': 0.7,
-            'hsv_v': 0.4,
-        }
-
-    def print_config(self):
-        config_dict = {key: value for key, value in self.__dict__.items() if not key.startswith('__')}
-        for k, v in config_dict.items():
-            print("{} : {}".format(k, v))
-
-# YOLOv7-S
-class Yolov7AFTConfig(Yolov7AFBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 0.50
-        self.scale = "t"
-        self.use_pretrained = True
-        self.fpn_expansions = [0.5, 0.5]
-        self.fpn_block_bw = 2
-        self.fpn_block_dw = 1
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.0
-        self.copy_paste  = 0.5
-
-# YOLOv7-L
-class Yolov7AFLConfig(Yolov7AFBaseConfig):
-    def __init__(self) -> None:
-        super().__init__()
-        # ---------------- Model config ----------------
-        self.width = 1.0
-        self.scale = "l"
-        self.fpn_expansions = [0.5, 0.5]
-        self.fpn_block_bw = 4
-        self.fpn_block_dw = 1
-
-        # ---------------- Data process config ----------------
-        self.mosaic_prob = 1.0
-        self.mixup_prob  = 0.1
-        self.copy_paste  = 0.5