Comments (13)
This is very hard to see. Please summarize your problem.
from posefix_release.
During the training process, the train loss did't converge and the effectivity of the algorithm became worser and worser
from posefix_release.
some time the loss become very large:
and some time being negative:
from posefix_release.
Hmm. I haven't met that problem. Which dataset are you using?
from posefix_release.
COCO
from posefix_release.
Your downloaded code is the most recent version?
from posefix_release.
Did you change
## dataset
dataset = 'PoseTrack' # 'COCO', 'PoseTrack', 'MPII'
testset = 'val' # train, test, val (there is no validation set for MPII)
to your desired settings? (COCO, train)
from posefix_release.
I downloaded the code on the 8th of August, I suppose it is the latest code
from posefix_release.
dataset = 'COCO' # 'COCO', 'PoseTrack', 'MPII'
testset = 'val' # train, test, val (there is no validation set for MPII)
I did't change the testset and suppose it is a params only for testing process
from posefix_release.
Right. It seems the number of iteration for one epoch is smaller than mine. Do you use other version of COCO? Anyway, I haven't met this problem, so I cannot give you clear answer. Sorry. I seems there is some issue with heatmap loss. Could you debug yourself and let me know any progress? Thank you!
One thing I want to check is your trained model gives bad performance because of this issue?
from posefix_release.
ok, as i have modified the code as discussion in issue #8 to fixed the bug of InvalidArgumentError
Could you please provide me with the change of loss during the training process?
from posefix_release.
Sorry I don't keep training log :(
from posefix_release.
Never mind, thanks for your patience!
from posefix_release.
Related Issues (20)
- About how to apply this work on my own data? HOT 17
- calculate miss_prob , jitter_prob and inv_prob on other dataset HOT 1
- Statistics on latency and throughput HOT 1
- demo for a single image HOT 4
- About Fig.5 in your paper HOT 2
- Can we directly implement Posefix on Inference phase or should we have to do it from training itself? HOT 3
- Combining all the three datasets HOT 3
- I can't use my GPU? HOT 1
- Find data HOT 6
- converting model to ONNX for C++ inference with TensorRT HOT 3
- Is there any plan to provide pre-trained weights available? HOT 2
- inefficient heavy-CPU training HOT 7
- input image normalization HOT 4
- the reason behind the usage of render_onehot_heatmap for training target instead of render_gaussian_heatmap HOT 3
- the usage of the softmaxed heatmaps as the keypoint confidence scores
- Format of result.json HOT 1
- Missing modules in requirements
- FileNotFoundError: [Errno 2] No such file or directory: 'tmp_result_0.pkl' and IndexError: list index out of range HOT 3
- tensorflow 2
- I appreciate your work!
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from posefix_release.