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work_dirs/yolo3/yolov3.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:407e9e50f6014623e0ccbf216175b730ca353a60f13ebf87027dbb24a2021ffa
+size 493184217

work_dirs/yolo3/yolov3_d53_mstrain-608_273e_coco.py

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+_base_ = '../_base_/default_runtime.py'
+# model settings
+model = dict(
+    type='YOLOV3',
+    backbone=dict(
+        type='Darknet',
+        depth=53,
+        out_indices=(3, 4, 5),
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
+    neck=dict(
+        type='YOLOV3Neck',
+        num_scales=3,
+        in_channels=[1024, 512, 256],
+        out_channels=[512, 256, 128]),
+    bbox_head=dict(
+        type='YOLOV3Head',
+        num_classes=14,
+        in_channels=[512, 256, 128],
+        out_channels=[1024, 512, 256],
+        anchor_generator=dict(
+            type='YOLOAnchorGenerator',
+            base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                        [(30, 61), (62, 45), (59, 119)],
+                        [(10, 13), (16, 30), (33, 23)]],
+            strides=[32, 16, 8]),
+        bbox_coder=dict(type='YOLOBBoxCoder'),
+        featmap_strides=[32, 16, 8],
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_conf=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_xy=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=2.0,
+            reduction='sum'),
+        loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='GridAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0)),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        conf_thr=0.005,
+        nms=dict(type='nms', iou_threshold=0.45),
+        max_per_img=100))
+# dataset settings
+data_root = './'
+work_dir = './result/yolov3'
+load_from = 'result/yolov3/latest.pth'
+resume_from = 'result/yolov3/latest.pth'
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(608, 608),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+classes = ('person bev', 'car bev', 'van bev', 'bus bev', 'truck bev','aeroplane','train' , 'bird', 'boat', 'car', 'person', 'bus', 'truck','camouflage man')
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+
+        classes=classes,
+        ann_file='./final_train_dataset/label/train_final_with_js.json',
+        img_prefix='./final_train_dataset/images',
+        pipeline=train_pipeline),
+    val=dict(
+
+        classes=classes,
+        ann_file='./final_train_dataset/label/val_final_with_js.json',
+        img_prefix='./final_train_dataset/images',
+
+        pipeline=test_pipeline),
+    test=dict(
+
+        classes=classes,
+        ann_file='./final_train_dataset/label/val_final_with_js.json',
+        img_prefix='./final_train_dataset/images',
+
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=2000,  # same as burn-in in darknet
+    warmup_ratio=0.1,
+    step=[218, 246])
+# runtime settings
+# checkpoint resumed from 273
+runner = dict(type='EpochBasedRunner', max_epochs=300)
+evaluation = dict(interval=10, metric=['bbox'])
+
+log_config = dict(interval=100)
+checkpoint_config = dict(interval=10)
+
+seed = 0
+gpu_ids = range(1)