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Query About Training & Evaluation Strategy #11
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hi, we follow the previous work TCMR, which uses different checkpoints for evaluating on different datasets. |
Thank you for the clarification. However, I am still slightly confused. As per my understanding, in TCMR, the two different checkpoints are for 2 cases - (1) When they use 3DPW during training (Table-4), and (2) When they do not use 3DPW during training (Table-6). However, for table-4, they use a single checkpoint. However, in your case, there are 3 models - (1A) So, I understand that TCMR uses 2 different models for their table-4 and table-6. However, for table-4, they are using a single model. But in your case, for table-1 itself, there are 2 different checkpoint which you use, and then for table-2, there is a third checkpoint, right? |
Tcmr provides two configs about table 4 and the contents are different. Thus tcmr uses these two configs to train two models. In https://drive.google.com/drive/folders/1NxzmKw5QTGtOKgSQetkq66ZO-dbBSzrd, you can see that there are two checkpoints related to Table 4. |
Oh okay, I realise that I was mistaken about TCMR's strategy earlier. Hence the confusion. I understand it now. Thanks a lot for clarifying. By the way, I also noticed that in the evaluate.py file, in lines 142-146, for |
Hello,
Thank you for your interesting work, and for making the source code public.
Could you please clarify if you use the same pretrained checkpoint to evaluate your method on all 3 datasets (3DPW, Human3.6M and MPI-INF-3DHP) or do you use different models during evaluation? As per the paper, I thought that you are using a single checkpoint for evaluations, but looking at the evaluation instructions and this issue, I understand that you are using different checkpoints for evaluating on different datasets. Hence, I am confused. Kindly clarify.
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