Comments (6)
about 7 FPS in 3090
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about 7 FPS in 3090
I am wondering if that can be improved if we set it to run inference on like every 10-20 frames instead of every frame. Given that a crowd of people will most likely still be there after 1 or 2 secs.
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I tried several architectures and saw that timm-regnetx_064 encoder + PAN decoder from here https://github.com/qubvel/segmentation_models.pytorch
takes 9.76 GFlops vs 26.46 GFlops currnet hrnet, but results a little bit worse, 57.14 vs 59.29 on NWPU (2048) dataset
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Related Issues (20)
- About the training data set HOT 2
- about "the txt file in './local_eval/point_files/'," HOT 2
- Training set image size HOT 7
- loss HOT 7
- Code of data preparation for nwpu dataset not provided HOT 2
- I-SSIM Loss HOT 10
- 模型在训练时损失很大 HOT 2
- HRNet vs. VGG-16 HOT 1
- 视频测试出现问题 HOT 3
- 你好,代码似乎只用了L2 Loss没有用SSIM? HOT 1
- 1-SSIM 损失
- 请问XX_pred_fidt.txt这个预测位置的文档怎么生成,代码里好像没见有 HOT 1
- Has anyone deployed FIDTM in C++ HOT 2
- 请问可以提供下 nwpu 的generate代码吗 HOT 3
- Training on custom dataset HOT 2
- Huge or normal loss while training? HOT 2
- ISSIM-Loss HOT 1
- Queries on custom dataset HOT 2
- How to train on NWPU-Crowd dataset? Can you provide data preprocessing script? thank you HOT 1
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