_base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'

# Use the model trained on the COCO as the pretrained model
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'  # noqa

# dataset settings
data_root = 'data/CrowdHuman/'
dataset_type = 'YOLOv5CrowdHumanDataset'

# parameters that often need to be modified
num_classes = 1

anchors = [
    [(6, 14), (12, 28), (19, 48)],  # P3/8
    [(29, 79), (46, 124), (142, 54)],  # P4/16
    [(73, 198), (124, 330), (255, 504)]  # P5/32
]

model = dict(
    bbox_head=dict(
        head_module=dict(num_classes=num_classes),
        prior_generator=dict(base_sizes=anchors)))

train_dataloader = dict(
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotation_train.odgt',
        data_prefix=dict(img='Images/')))

val_dataloader = dict(
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotation_val.odgt',
        data_prefix=dict(img='Images/'),
        # CrowdHumanMetric does not support out-of-order output images
        # for the time being. batch_shapes_cfg does not support.
        batch_shapes_cfg=None))
test_dataloader = val_dataloader

val_evaluator = dict(
    _delete_=True,
    type='mmdet.CrowdHumanMetric',
    ann_file=data_root + 'annotation_val.odgt',
    metric=['AP', 'MR', 'JI'])
test_evaluator = val_evaluator