轻量化神经网络测试
尝试训练100张640x480大小的图片
| 网络模型 | Time(s) | Images/s |
|---|---|---|
| Openpose(VGG-19) | 36.4 | 2.75 |
| Openpose(MobileNet v1) | 4.2 | 23.94 |
尝试训练100张224x224大小的图片
| 网络模型 | Time(s) | Images/s |
|---|---|---|
| Openpose(VGG-19) | 10.45 | 9.57 |
| Openpose(MobileNet v1) | 1.9 | 53.68 |
在224x224分辨率图像输入下网络的参数量和运算量
| 网络模型 | 参数量(Million) | GFLOPs |
|---|---|---|
| Openpose(VGG-19) | 52.31 | 50.41 |
| Openpose(MobileNet v1) | 2.88 | 50.41 |
使用验证集(Val )进行验证
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.400
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.660
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.407
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.338
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.494
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.462
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.698
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.476
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.605