/
nanodet-plus-m_416-yolo.yml
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/
nanodet-plus-m_416-yolo.yml
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# nanodet-plus-m_416
# COCO mAP(0.5:0.95) = 0.304
# AP_50 = 0.459
# AP_75 = 0.317
# AP_small = 0.106
# AP_m = 0.322
# AP_l = 0.477
save_dir: workspace/nanodet-plus-m_416
model:
weight_averager:
name: ExpMovingAverager
decay: 0.9998
arch:
name: NanoDetPlus
detach_epoch: 10
backbone:
name: ShuffleNetV2
model_size: 1.0x
out_stages: [2,3,4]
activation: LeakyReLU
fpn:
name: GhostPAN
in_channels: [116, 232, 464]
out_channels: 96
kernel_size: 5
num_extra_level: 1
use_depthwise: True
activation: LeakyReLU
head:
name: NanoDetPlusHead
num_classes: 80
input_channel: 96
feat_channels: 96
stacked_convs: 2
kernel_size: 5
strides: [8, 16, 32, 64]
activation: LeakyReLU
reg_max: 7
norm_cfg:
type: BN
loss:
loss_qfl:
name: QualityFocalLoss
use_sigmoid: True
beta: 2.0
loss_weight: 1.0
loss_dfl:
name: DistributionFocalLoss
loss_weight: 0.25
loss_bbox:
name: GIoULoss
loss_weight: 2.0
# Auxiliary head, only use in training time.
aux_head:
name: SimpleConvHead
num_classes: 80
input_channel: 192
feat_channels: 192
stacked_convs: 4
strides: [8, 16, 32, 64]
activation: LeakyReLU
reg_max: 7
class_names: &class_names ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports_ball', 'kite', 'baseball_bat',
'baseball_glove', 'skateboard', 'surfboard', 'tennis_racket',
'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush']
data:
train:
name: YoloDataset
img_path: coco/train2017
ann_path: coco/train2017
class_names: *class_names
input_size: [416,416] #[w,h]
keep_ratio: False
pipeline:
perspective: 0.0
scale: [0.6, 1.4]
stretch: [[0.8, 1.2], [0.8, 1.2]]
rotation: 0
shear: 0
translate: 0.2
flip: 0.5
brightness: 0.2
contrast: [0.6, 1.4]
saturation: [0.5, 1.2]
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
val:
name: YoloDataset
img_path: coco/val2017
ann_path: coco/val2017
class_names: *class_names
input_size: [416,416] #[w,h]
keep_ratio: False
pipeline:
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
device:
gpu_ids: [0]
workers_per_gpu: 10
batchsize_per_gpu: 96
schedule:
# resume:
# load_model:
optimizer:
name: AdamW
lr: 0.001
weight_decay: 0.05
warmup:
name: linear
steps: 500
ratio: 0.0001
total_epochs: 300
lr_schedule:
name: CosineAnnealingLR
T_max: 300
eta_min: 0.00005
val_intervals: 10
grad_clip: 35
evaluator:
name: CocoDetectionEvaluator
save_key: mAP
log:
interval: 50