pytorch-cpn
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GengDavid
Training with other configurations.
Hi @GengDavid,
Thanks for the great implementation. I'm eager collaborate with you to test other configurations. I have 2 x 1080 and 2 x 1080ti. I can borrow more if needed. Looking forward to your response!
Hi @mkocabas ,
Thanks for your interest in my implementation.
There may be at least two configurations to be tested, ResNet-50+384x288 and ResNet-101+384x288. Which one do you prefer to test? Or do you want to test both of them?
I've modified the codes a little, so please clone/pull the latest version before you run it. Please follow README to configure the environment.
You can train a ResNet-50+384x288 model directly in 384.288.model dir. by running train.py
You may need to modify batch size in config.py, and use -g to specify the number of GPU you use. For example, you may set batch_size = 12 and run python3 train.py -g 2 when you use 2 x 1080 gpu to train the model.
To train a ResNet-101+384x288 model, you need to set model='CPN101' in config.py, and then follow the same way to train the model.
If you have any questions, feel free to contact me. You can also mail me at [email protected] or [email protected].
Cool, so I can start with ResNet-50+384x288. After that I can try ResNet-101.
I'll use 2 x 1080ti with the default hyperparameters as in config. Am I correct?
@GengDavid we have a little problem. 1080tis have 11GB memory. batch_size=6 barely fits the memory. This means that we can train with batch_size=12 using 2 gpus. What do you think?
If you are using 1080tis, I think you can set batch_size more than 12 with 2 gpus while running ResNet-50+384x288 model.
@mkocabas ResNet-50+384x288 model with batch_size=12 takes about 8G memory in my experiment.
I'm consistently getting OOM error, but let me check. I'll restart the computer, maybe there are some blocking processes. I'll inform you about the progress.
@GengDavid, restarting solved the problem. Thanks for pointing out! I'll update this issue as training continues.
How many epochs did you train the 256x192 model?
Epoch 6 (tested with GT bboxes)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.688
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.894
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.750
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.654
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.742
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.719
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.904
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.776
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.681
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.777
Epoch 13 (tested with GT bboxes)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.726
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.914
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.785
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.690
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.781
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.754
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.924
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.810
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.716
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.812
@GengDavid do you have the weights of 5th epoch of ResNet50-256x192 model?
Yes, I do have saved the 5th epoch pre-trained model.
But I'm sorry to tell you that there's something different from the original paper in my code just as @Tiamo666 mentioned in issue #4.
The results seem very close, but I'm still going to modify the network and then re-test it.
Yeah I saw the discussion. Please let me know about the results after modification. If you don't have enough GPUs, I can test the corrected model.
I'll let you know the results but it may take a little long time since I only have 1*1080 free to run the code. May be you can test test the ResNet-50+384x288 model first.
Thanks!
I've started to train fixed ResNet-50+384x288 on a Titan V w batch-size=24
Hi, @mkocabas
I've updated the ResNet-50+256*192 results. Have got some results?
Thx.
Hi, David, I've trained with the ResNet-50+384*288 with ground truth bboxes. The test result of 32 epoch is as follows: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.737 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.915 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.806 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.706 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.792 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.767 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.929 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.826 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.729 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.824
Due to the limit of network, I can not download the person detections results on COCO successfully, So I just use the ground truth.
@Tiamo666 Great job! Can you provide the pre-trained model so that I can test it with detection results? I think you can open a PR with the a link on it to download pre-trained model.
@Tiamo666 Or if you do not want to open a RP, could you just provide a link to download the model? Google Drive, Onedrive, Dropbox and Baidu Yun are all fine.
OK，I guess Baidu yun is a good choice. I will try to share the pretrained model on it and provide you the link as soon as I uploaded model
hi，David, I've already uploaded the model on BaiduYun. Here is the link: https://pan.baidu.com/s/1fdy5_0HQm63QtlOzxKbpuw
That's cool! I'll have time to train with Resnet101+384*288, I'll share the model after finishing training
@Tiamo666 That's great! If you have any problem, feel free to contact me.
Hi, David. I've uploaded the model of cpn384*288 with Resnet101 on Baidu Yun. Here is the link: https://pan.baidu.com/s/1toikUHSqHhHP3DkIOkNctA
Hello, David, I've just found that I trained with the old code which has "Color Normalized bug" last week. I feel sorry for that, I could retrain the model this week.
@Tiamo666 Retraining it is a better choice but may cost more time. I think we can just fine-tune the trained model. This may influence the result a little but can save time. However, I currently do not have free GPUs to do this work. What do you think about that?