Open dragonfly90 opened 6 years ago
boxsize = 368 scale_search = 0.5, 1, 1.5, 2 multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] padRightCorner if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4: deleteIdx.append(i) interpolation = cv.INTER_CUBIC
starting_range = 0.85; %0.25 0.7 ending_range = 1.5; %1.2 1.8 octave = 6; starting_scale = boxsize/(size(oriImg,1)ending_range); ending_scale = boxsize/(size(oriImg,1)starting_range); multiplier = 2.^(log2(starting_scale):(1/octave):log2(ending_scale));
Scale: default 'bicubic'
Inference if score > -100
padHeight if (subset(i,end)<3) || (subset(i,end-1)/subset(i,end)<0.2) deleIdx = [deleIdx;i]; end
not working num 8
boxsize = 368 scale_search = [0.5, 1, 1.5, 2] multiplier = [x boxsize1.0/ oriImg.shape[0] for x in scale_search]
DONE (t=0.06s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.474 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.701 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.527 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.508 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.509 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.718 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.514 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.507 None 123.940952067
Python accuracy: DONE (t=0.06s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.550 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.800 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.610 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.541 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.576 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.591 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.812 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.644 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.651 None 114.388944797
Below is matlab detailed accuracy: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.577 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.797 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.627 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.545 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.629 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.621 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.814 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.662 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.555 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.706
Evaluation: GT bbox + CPM: 63 SSD + CPM: 53 method of Realtime Pose paper: 58.5
Mask RCNN: around 63 at test dataset G-RMI: 64.9 Associative Embedding: 65.5
not working number 8 [492395, 132791, 100896, 559665, 134206, 560349, 429633, 451095]
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.551 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.801 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.611 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.545 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.579 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.595 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.818 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.649 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.654
We've been playing with this network in Pytorch.
I'm interested in what your loss looks like, have you graphed it, or have a sample of the numbers?
Also how long does it train for, I'm running it but there is much output info.
Thanks,
@lukemurray I am not sure about it. I only calculate the last layer loss. The sum of heat map and part affinity graph mean is about 200~300. How about yours?
Similar range to want we get as well.
@kohillyang How is the training going now? Thanks.
@dragonfly90 @lukemurray I got fewer loss about the issue, but I change the network and get the smaller model and faster network, in stage 6 the L1-loss is about 50 and the L2-loss is about 15. the total loss is about 422
Hi @dragonfly90 I'm confused by code of the author in python-demo. I would like to compute the mAP of the model trained by myself, but I didn't clearly know how to get the output format like coco-key point evaluation . Did you compute the mAP of your model by python-caffe, I have not the matlab-caffe but only python-caffe. Any answer is appreciated. Looking forward your answer
@Ai-is-light You mean of caffe model? I only used the mxnet model. What mAP did you get? Evaluation on coco validation dataset with transfered mxnet model: evaluation_coco.py
@dragonfly90 'Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.801'。I have a tensorflow model about openpose. but when evaluation the model, I can't reproduce the results in the paper. Your results seems similar, so could you tell me which scale you used?Thanks!
python evaluation
boxsize = 368 scale_search = 0.5, 1, 1.5, 2 multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] padRightCorner if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4: deleteIdx.append(i) interpolation = cv.INTER_CUBIC
matlab evaluation
starting_range = 0.85; %0.25 0.7 ending_range = 1.5; %1.2 1.8 octave = 6; starting_scale = boxsize/(size(oriImg,1)ending_range); ending_scale = boxsize/(size(oriImg,1)starting_range); multiplier = 2.^(log2(starting_scale):(1/octave):log2(ending_scale));
Scale: default 'bicubic'
Inference if score > -100
padHeight if (subset(i,end)<3) || (subset(i,end-1)/subset(i,end)<0.2) deleIdx = [deleIdx;i]; end
Can you please tell me the config data to test the MPI model in python. The config file only has COCO evaluation paramters. Thank You
Hard to reproduce the result mentioned in Cao's original paper, they talk about it in the original implementation. https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation/issues/35 https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation/issues/68 https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation/issues/72 https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation/issues/74
The matlab version should be 0.577
Python I obtained an average precision of 0.43
Matlab (1 scale) 48.2 Matlab (4 scales) 57.7 caffe_rtpose (1 scale) 44.9
My current implementation starting_range = 0.85; %0.25 0.7 ending_range = 1.5; %1.2 1.8 octave = 6; starting_scale = boxsize/(size(oriImg,1)ending_range); ending_scale = boxsize/(size(oriImg,1)starting_range); multiplier = 2.^(log2(starting_scale):(1/octave):log2(ending_scale));
Accumulating evaluation results... DONE (t=0.06s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.400 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.648 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.423 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.377 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.443 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.436 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.663 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.455 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.502 None 119.816125202