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yolov5s_qat_dis.yaml
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yolov5s_qat_dis.yaml
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Global:
model_dir: ./yolov5s.onnx
image_path: None # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path.
coco_dataset_dir: dataset/coco/
coco_train_image_dir: train2017
coco_train_anno_path: annotations/instances_train2017.json
coco_val_image_dir: val2017
coco_val_anno_path: annotations/instances_val2017.json
arch: YOLOv5
Distillation:
alpha: 1.0
loss: soft_label
QuantAware:
onnx_format: true
use_pact: true
activation_quantize_type: 'moving_average_abs_max'
quantize_op_types:
- conv2d
- depthwise_conv2d
TrainConfig:
train_iter: 3000
eval_iter: 1000
learning_rate: 0.00001
optimizer_builder:
optimizer:
type: SGD
weight_decay: 4.0e-05
target_metric: 0.365