Closed zwithz closed 3 years ago
Hi,
You can test the below codes work well
for e in range(10):
print(e)
print(e)
This means we can refer e
outside the for loop.
What is your current precision? Which datasets did you use for the training?
This means we can refer e outside the for loop.
Okay, Python... 😳
Here you go, did you train your model based on backbone ResNet 101 or something? The following results are based on ResNet 50.
Dataset | Epoch | Time | Speed (it/s) | $AP^{BOX}$ (%) |
---|---|---|---|---|
Baseline | 43.80 | |||
MuPoTS | 0 | 4:07 | 1.52 | 19.28 |
1 | 2:31 | 1.07 | 23.41 | |
2 | 2:36 | 1.07 | 24.97 | |
3 | 2:32 | 1.07 | 29.92 | |
4 | 2:33 | 1.06 | 31.72 | |
5 | 2:35 | 1.05 | 33.29 | |
6 | 2:31 | 1.08 | 31.80 | |
7 | 2:32 | 1.07 | 29.69 | |
8 | 2:27 | 1.11 | 34.75 | |
9 | 2:20 | 1.16 | 31.70 | |
10 | 2:18 | 1.18 | 31.87 | |
11 | 2:21 | 1.15 | 33.80 | |
12 | 2:23 | 1.14 | 35.45 | |
13 | 2:21 | 1.15 | 25.34 | |
14 | 2:22 | 1.15 | 34.55 | |
15 | 2:20 | 1.16 | 39.24 | |
16 | 2:23 | 1.14 | 36.85 | |
17 | 2:22 | 1.14 | 35.46 | |
18 | 2:18 | 1.17 | 33.22 | |
19 | 2:20 | 1.16 | 34.09 |
Dataset | Epoch | Time | Speed (it/s) | $AP^{root}_{25}$ (%) |
---|---|---|---|---|
Baseline | 28.50 | |||
MuPoTS | 0 | 6:54 | 1.30 | 14.61 |
1 | 6:39 | 1.35 | 24.18 | |
2 | 6:45 | 1.34 | 24.86 | |
3 | 6:44 | 1.34 | 29.28 | |
4 | 6:45 | 1.33 | 26.47 | |
5 | 6:42 | 1.34 | 29.96 | |
6 | 6:37 | 1.36 | 30.34 | |
7 | 6:36 | 1.37 | 32.85 | |
8 | 6:47 | 1.33 | 33.15 | |
9 | 6:46 | 1.33 | 31.04 | |
10 | 6:43 | 1.34 | 29.09 | |
11 | 6:33 | 1.38 | 32.77 | |
12 | 6:40 | 1.34 | 33.70 | |
13 | 6:45 | 1.34 | 31.85 | |
14 | 6:44 | 1.34 | 34.97 | |
15 | 6.49 | 1.32 | 32.55 | |
16 | 6:57 | 1.29 | 33.42 | |
17 | 6:50 | 1.32 | 34.47 | |
18 | 6:25 | 1.31 | 31.85 | |
19 | 7:38 | 1.18 | 32.79 |
Dataset | Epoch | Time | Speed (it/s) | MRPE | MRPE_x | MRPE_y | MRPE_z |
---|---|---|---|---|---|---|---|
Baseline | 0.386 | 0.045 | 0.094 | 0.353 | |||
3DPW | 0 | 2:53 | 1.60 | 0.563 | 0.070 | 0.143 | 0.504 |
1 | 2:48 | 1.65 | 0.541 | 0.069 | 0.138 | 0.483 | |
2 | 2:48 | 1.65 | 0.491 | 0.061 | 0.116 | 0.448 | |
3 | 2:49 | 1.64 | 0.542 | 0.065 | 0.136 | 0.489 | |
4 | 2:48 | 1.65 | 0.519 | 0.059 | 0.122 | 0.475 | |
5 | 2:49 | 1.64 | 0.444 | 0.055 | 0.109 | 0.401 | |
6 | 2:48 | 1.65 | 0.448 | 0.057 | 0.106 | 0.407 | |
7 | 2:48 | 1.65 | 0.418 | 0.055 | 0.109 | 0.377 | |
8 | 2:50 | 1.63 | 0.478 | 0.055 | 0.112 | 0.438 | |
9 | 2:50 | 1.63 | 0.543 | 0.061 | 0.113 | 0.506 | |
10 | 2:46 | 1.67 | 0.491 | 0.060 | 0.114 | 0.451 | |
11 | 2:49 | 1.64 | 0.481 | 0.053 | 0.112 | 0.442 | |
12 | 2:49 | 1.64 | 0.495 | 0.056 | 0.107 | 0.459 | |
13 | 2:49 | 1.64 | 0.432 | 0.050 | 0.099 | 0.397 | |
14 | 2:48 | 1.65 | 0.503 | 0.055 | 0.098 | 0.470 | |
15 | 2:49 | 1.64 | 0.448 | 0.054 | 0.097 | 0.415 | |
16 | 2:48 | 1.65 | 0.440 | 0.055 | 0.096 | 0.407 | |
17 | 2:48 | 1.65 | 0.460 | 0.054 | 0.095 | 0.428 | |
18 | 2:50 | 1.63 | 0.462 | 0.054 | 0.097 | 0.429 | |
19 | 3:00 | 1.54 | 0.437 | 0.053 | 0.095 | 0.405 |
Dataset | Epoch | Time | Speed (it/s) | MRPE | MRPE_x | MRPE_y | MRPE_z | Directions | Discussion | Eating | Greeting | Phoning | Posing | Purchases | Sitting | Sitting Down | Smoking | Photo | Waiting | Walking | Walk Dog | Walk Together |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Baseline | 120.00 | 23.3 | 23.0 | 108.1 | ||||||||||||||||||
Human3.6M | 0 | 0:41 | 1.66 | 128.30 | 33.83 | 47.78 | 98.24 | 75.68 | 103.26 | 118.83 | 106.74 | 129.65 | 80.36 | 100.74 | 204.66 | 283.18 | 132.22 | 121.89 | 104.13 | 90.10 | 152.61 | 96.89 |
1 | 0:36 | 1.86 | 148.07 | 28.39 | 27.99 | 133.46 | 124.77 | 126.71 | 153.52 | 132.18 | 139.22 | 115.90 | 145.83 | 184.37 | 244.23 | 160.95 | 133.51 | 123.70 | 134.60 | 166.31 | 134.66 | |
2 | 0:36 | 1.86 | 141.87 | 26.95 | 29.71 | 127.02 | 130.51 | 119.52 | 154.68 | 128.15 | 132.46 | 118.38 | 138.51 | 180.12 | 251.67 | 145.65 | 130.00 | 115.84 | 108.41 | 162.61 | 114.06 | |
3 | 0:36 | 1.88 | 104.33 | 25.68 | 30.44 | 84.89 | 68.53 | 85.59 | 115.64 | 103.10 | 101.17 | 70.79 | 102.51 | 143.14 | 193.27 | 100.63 | 106.72 | 95.44 | 75.45 | 121.34 | 80.66 | |
4 | 0:36 | 1.88 | 177.32 | 27.48 | 30.24 | 166.32 | 166.79 | 160.97 | 186.02 | 157.22 | 171.35 | 155.79 | 176.81 | 208.59 | 270.88 | 189.83 | 172.30 | 142.99 | 146.76 | 199.91 | 148.10 | |
5 | 0:35 | 1.91 | 153.15 | 26.46 | 29.38 | 141.45 | 128.27 | 124.61 | 153.30 | 135.66 | 138.08 | 120.57 | 162.19 | 178.03 | 340.77 | 147.35 | 146.93 | 126.21 | 116.44 | 184.68 | 121.23 | |
6 | 0:35 | 1.90 | 124.33 | 24.84 | 26.26 | 110.89 | 125.75 | 114.95 | 111.07 | 128.06 | 103.07 | 117.14 | 135.63 | 127.50 | 197.39 | 116.56 | 128.53 | 119.17 | 115.38 | 141.77 | 117.53 | |
7 | 0:34 | 1.95 | 124.65 | 24.55 | 25.97 | 111.31 | 92.01 | 104.58 | 134.57 | 107.96 | 126.24 | 89.02 | 116.49 | 164.28 | 239.92 | 131.30 | 119.69 | 101.37 | 93.26 | 142.97 | 91.37 | |
8 | 0:35 | 1.91 | 127.66 | 24.77 | 26.11 | 114.54 | 131.45 | 114.40 | 122.83 | 132.40 | 117.81 | 122.17 | 127.14 | 133.71 | 189.32 | 133.62 | 122.78 | 120.70 | 110.27 | 133.39 | 112.70 | |
9 | 0:36 | 1.87 | 112.76 | 23.84 | 23.23 | 99.61 | 106.35 | 98.66 | 111.65 | 110.51 | 102.71 | 98.49 | 108.84 | 144.35 | 181.63 | 116.35 | 104.50 | 101.67 | 92.18 | 120.19 | 95.03 | |
10 | 0:35 | 1.92 | 97.15 | 23.15 | 22.76 | 82.93 | 81.34 | 88.57 | 89.45 | 99.72 | 83.58 | 78.10 | 100.22 | 105.95 | 195.36 | 92.19 | 94.14 | 96.28 | 75.48 | 108.98 | 80.65 | |
11 | 0:35 | 1.93 | 134.05 | 23.27 | 24.56 | 122.79 | 137.86 | 114.01 | 125.26 | 131.74 | 117.92 | 120.86 | 134.79 | 141.23 | 240.51 | 133.14 | 126.71 | 122.42 | 115.48 | 153.39 | 122.03 | |
12 | 0:35 | 1.90 | 136.22 | 23.07 | 23.29 | 126.31 | 130.01 | 116.07 | 134.56 | 128.38 | 129.03 | 116.16 | 138.07 | 155.80 | 235.10 | 142.73 | 126.19 | 116.39 | 113.95 | 151.77 | 117.99 | |
13 | 0:35 | 1.92 | 123.45 | 22.97 | 23.04 | 112.38 | 126.41 | 109.05 | 116.91 | 123.70 | 107.77 | 111.75 | 124.86 | 122.90 | 228.95 | 120.28 | 114.80 | 112.39 | 105.36 | 138.63 | 111.86 | |
14 | 0:36 | 1.87 | 136.61 | 24.06 | 23.36 | 126.42 | 143.47 | 116.17 | 130.49 | 139.71 | 123.66 | 131.57 | 133.79 | 140.94 | 223.92 | 134.35 | 126.87 | 124.56 | 126.09 | 152.46 | 128.56 | |
15 | 0:36 | 1.89 | 123.44 | 23.08 | 23.69 | 112.04 | 118.46 | 105.14 | 121.42 | 120.36 | 111.06 | 105.90 | 120.15 | 139.26 | 239.23 | 119.25 | 114.12 | 113.56 | 102.26 | 132.18 | 104.22 | |
16 | 0:36 | 1.85 | 135.81 | 23.50 | 23.18 | 125.48 | 124.58 | 113.57 | 136.85 | 123.99 | 127.35 | 114.28 | 141.05 | 159.83 | 248.83 | 139.19 | 125.14 | 117.31 | 108.58 | 155.80 | 112.53 | |
17 | 0:36 | 1.88 | 125.45 | 22.57 | 22.59 | 114.76 | 127.65 | 111.01 | 117.56 | 124.69 | 112.39 | 116.45 | 125.01 | 128.80 | 214.73 | 123.00 | 119.02 | 116.86 | 108.65 | 143.69 | 113.41 | |
18 | 0:34 | 1.95 | 122.62 | 22.18 | 23.05 | 111.71 | 125.34 | 111.14 | 114.35 | 124.14 | 109.78 | 114.81 | 122.42 | 123.42 | 203.16 | 120.58 | 117.28 | 115.30 | 107.16 | 137.71 | 111.64 | |
19 | 0:35 | 1.93 | 128.57 | 22.40 | 23.04 | 118.24 | 130.76 | 114.70 | 120.69 | 127.32 | 115.82 | 119.28 | 129.28 | 132.06 | 219.14 | 127.80 | 132.36 | 119.35 | 110.91 | 143.87 | 115.12 |
Please do not copy and paste all raw data.. just let me know your precision and training data. By the way, why there is AP^box? I don't train a human detection model.
Sorry for not stating clearly, AP^{box} means use_gt_bbox=True
here.
Train datasets | Test dataset | use_gt_bbox |
Your precision | My best precision |
---|---|---|---|---|
MuCo + MSCOCO | MuPoTS | True | 43.80(AP) | 39.24 |
MuCo + MSCOCO | MuPoTS | False | 28.5(AP) | 33.70 |
MuCo + MSCOCO | 3DPW | False | 0.386(MRPE) | 0.418 |
Human36M Protocol2 + MPII | Human36M | False | 120.0(MRPE) | 97.15 |
All data above are trained/tested on RootNet models, the data on PoseNet is more inconsistent.
I see. You'd better check all snapshots, saved during the training stage. The accuracy of RootNet is not very stable due to the high depth ambiguity. However, I confirmed that the performance of PoseNet is stable. Could you let me know your PoseNet results?
The testing results of all the snapshots were here, and I tested several times, the results stayed almost the same. https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE/issues/31#issuecomment-795126548
Train datasets | Test dataset | Eval Metric | Your precision | My best precision |
---|---|---|---|---|
MuCo + MSCOCO | MuPoTS | Sequence-wise 3DPCK_{rel} & Accuracy for all groundtruths | 81.8(Avg) | 79.6 |
MuCo + MSCOCO | MuPoTS | Sequence-wise 3DPCK_{rel} & Accuracy only for matched groundtruths | 82.5(Avg) | 80.91 |
Human36M Protocol2 + MPII | Human36M | MPJPE | 53.3 | 54.34 |
Maybe all of the differences between your precision and mine are negligible in some way? I should hack the eval program to compute the average precision for me, otherwise, there will be too many csv files to be handled. Thanks for your prompt reply! Hope you have a great day~
So it seems your RootNet results are better than me? MuCo + MSCOCO | MuPoTS | False | 28.5(AP) | 33.70 Human36M Protocol2 + MPII | Human36M | False | 120.0(MRPE) | 97.15
I think PoseNet results differences are not that major, but I'm not sure. Please let me know if you can't catch up my results. Thanks!
Hello, I have been reviewing your paper and code (RootNet & PoseNet) for several days. I'd like to mention that the learning rate decrease code is implemented in the wrong way.
For instance, Line 77 used a local variable
e
, I guess that line 78-84 need to be indented by 4 spaces? https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE/blob/8bef0cd332c3423050a6f3b382d2a574623e1ffa/common/base.py#L77Your Code
I guess this is the right code? 🤔
BTW, I trained/tested several times and used several protocols and datasets (Human3.6M Protocol2 / MuCo / 3DPW), but I can not reproduce the precision you have mentioned in the README file or paperwork? Maybe the problem above caused it?