chenyanyin
commented
5 years ago i have output the results, groundtruth: "keypoints":[958.0,737.0,2,947.0,741.0,2,947.0,739.0,2,899.0,742.0,2,895.0,759.0,2,879.0,758.0,2,884.0, 739.0,2,946.0,732.0,2,946.0,734.0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] model outputs: "keypoints": [957.074, 735.728, 1.0, 947.816, 741.9, 1.0, 944.73, 738.814, 1.0, 899.47, 740.871, 1.0, 895.355, 759.387, 1.0, 878.897, 758.358, 1.0, 886.098, 737.785, 1.0, 942.673, 734.699, 1.0, 940.616, 730.585, 1.0, 943.702, 733.671, 1.0, 946.788, 733.671, 1.0, 876.84, 757.329, 1.0, 891.241, 739.842, 1.0, 884.04, 738.814, 1.0, 947.816, 741.9, 1.0, 939.587, 730.585, 1.0, 942.673, 734.699, 1.0]
i am confused about this result.
hello, i have trained my datasets by this code, but the result is very strange, it seems can't learning anything. i had check my datasets seriously, it's right, could you give me some advices for solve this. the result is shown when i trained about 40 epochs. Any suggestions from you will give me a lot of help, thank you very much.
Evaluate annotation type keypoints DONE (t=52.07s). Accumulating evaluation results... DONE (t=11.49s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.001 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.001 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.057 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.080 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.060 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.056 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.061