- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md).
- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
### Slim PP-YOLO
| Model | GPU number | images/GPU | Prune Ratio | Teacher Model | Model Size | input shape | Box AP<sup>val</sup> | Kirin 990(FPS) | download | config |
| Model | GPU number | images/GPU | Prune Ratio | Teacher Model | Model Size | input shape | Box AP<sup>val</sup> | Kirin 990 1xCore(FPS) | download | inference model download | config |
- Slim PP-YOLO is trained by slim traing method from [Distill pruned model](../../slim/extentions/distill_pruned_model/README.md)，distill training pruned PP-YOLO_MobileNetV3_small model with PP-YOLO_MobileNetV3_large model as the teacher model
- Pruning detectiom head of PP-YOLO model with ratio as 75%, while the arguments are `--pruned_params="yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights" --pruned_ratios="0.75,0.75,0.75,0.75"`
- For Slim PP-YOLO training, evaluation, inference and model exporting, please see [Distill pruned model](../../slim/extentions/distill_pruned_model/README.md)
