ssd_mobilenetv3_gpu.yaml

# system
mode: 0
distribute: True
num_parallel_workers: 8
enable_modelarts: False
eval_while_train: False

# dataset
dataset: "coco"
data_dir: "/data1/coco_ori"
shuffle: True
batch_size: 32
drop_remainder: True
num_classes: 81
classes: ['background', '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']

# Training options
image_size: [300, 300]
num_ssd_boxes: 1917
match_threshold: 0.5
nms_threshold: 0.6
min_score: 0.1
max_boxes: 100
all_reduce_fusion_config: [29, 58, 89]

# model
backbone: "mobilenet_v3_large_100"
backbone_ckpt_path: "./checkpoints/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt"
backbone_ckpt_auto_mapping: True
backbone_features_only: True
backbone_out_indices: [12, 16]

ckpt_path: "./ckpt/best.ckpt"

num_default: [3, 6, 6, 6, 6, 6]
extras_in_channels: [256, 672, 960, 512, 256, 256]
extras_out_channels: [672, 960, 512, 256, 256, 128]
extras_strides: [1, 1, 2, 2, 2, 2]
extras_ratio: [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
feature_size: [19, 10, 5, 3, 2, 1]
min_scale: 0.2
max_scale: 0.95
aspect_ratios: [[], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]
steps: [16, 32, 64, 100, 150, 300]
prior_scaling: [0.1, 0.2]
gamma: 2.0
alpha: 0.75
epoch_size: 500

ckpt_save_dir: "./ckpt"
keep_checkpoint_max: 20
eval_interval: 1
eval_start_epoch: 350
dataset_sink_mode: True
amp_level: "O0"

# scheduler
lr: 0.05
lr_init: 0.001
lr_end_rate: 0.001
warmup_epochs: 2

# optimizer
loss_scale: 1024.0
weight_decay: 0.00015
momentum: 0.9