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Pretrained model #12
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We cannot provide pre-trained model for face-mask-detection. |
Okay, I'll try with a bigger batch size because I trained with a GTX1070
and batch size 8. Probably that's the problem with mAP in no-masked. By the
way the error with a bigger batch size was an allocation memory error.
So I think if you can't provide the trained model maybe you can help me
with the optimal parameters for an i7 cpu + 16 gb ram + gtx1070. Another
way I think it's possible is using Google Collab or allocating a vm in
azure or aws to use a Tesla V100.
Any suggestions or help is appreciated.
Thanks in advance for your help.
…On Thu, Sep 17, 2020, 1:25 PM Amey Kulkarni ***@***.***> wrote:
We cannot provide pre-trained model for face-mask-detection.
What is mAP you are achieving currently? What are the errors? Based on you
batch size and hyper-parameters, your mAP might drop.
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With 24 Batch Size, you will need about 15 GB memory, whereas once you prune model (as in git ~12% prune ratio) you can be okay with 8GB memory. I have personally tried AWS and used Tesla V100 for this task and it worked out pretty well. I have not tried any other Cloud instances yet so cannot comment on it. |
Okay, but I didn't get a point. When you say about prune the model I think you're talking about infer. But my problem is during the training or I misunderstand you sorry for that. Again, if I can train with my own GPU to move then to my AGX Xavier it will be great. Just to put some context, I'm trying to learn how to build the workflow pipeline with my personal computer and the Xavier before moving to cloud. Following the idea of this repo: If I can train the model in my GTX1070 and move the model to infer in the Jetson then mission completed by now. After that I can think in a way to deploy via docker or something like that. |
I feel it should be doable even with GTX1070, you need to experiment with other hyper-params as well when you reduce batch size, in
In TLT, we do training first and once we achieve satisfactory accuracy, we prune model and retrain the pruned model. See 3 to 8 steps on TLT workflow |
Oh I got it now! I'll play a little with that this weekend and update this
if I succeed.
Thanks for your help!
…On Thu, Sep 17, 2020 at 2:38 PM Amey Kulkarni ***@***.***> wrote:
I feel it should be doable even with GTX1070, you need to experiment with
other hyper-params as well when you reduce batch size, in
detectnet_v2_train_resnet18_kitti.txt look at model_config
<https://github.com/NVIDIA-AI-IOT/face-mask-detection/blob/master/tlt_specs/detectnet_v2_train_resnet18_kitti.txt#L73>
. I had better luck with even batch size 16 previously. As batch size
reduces your training time will also increase.
When you say about prune the model I think you're talking about infer.
In TLT, we do training first and once we achieve satisfactory accuracy, we
prune model and retrain the pruned model. See 3 to 8 steps on TLT workflow
<https://github.com/NVIDIA-AI-IOT/face-mask-detection#nvidia-transfer-learning-toolkit-tlt-training-flow->
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Okay, working around with the GTX1070 I didn't find a good result in any possible way, I don't know exactly why. My suspicious points to my dataset and probably the batch size, but without luck to find a scientific answer.
after this I think: Okay, let's try retraining the pruned model with the default settings from the notebook and I get this:
I think I've misunderstood something, so I decided to make some changes. First, I changed the amount of the images for each class to 12000 and the results are quite better:
then I retrained with a tweak in the prune step, I changed this cell:
I changed the -pth from 0.8 to 0.12 and I get this result:
At this point I don't know exactly what to change or where point the effort to get closer to your results, If you can help me with this I'll really appreciate your help. NOTE: You should add a warning in the repo about the changes in the path in the config files for those who like me doesn't know much about TLT and stuff like that EDIT: I miss clarify about the configurations. I used the default config files provided in this repo with the change in the point where the models and data are located |
@fiv21 Thanks for working through this and recommendations. I am trying to add more detailed steps and examples with sample video soon in upcoming weeks, if that helps. |
That's great! |
Hi!
I would like to ask if you can share the pre-trained model for Jetson with the calibration file. I mean if you can share a .zip with the model trained by you and I can prune in my side.
The main problem I have is not achieving the same values in mAP even if I retrain.
Thanks in advance!
All the best,
Franco.
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