├── Inference_speed.ipynb ├── LICENSE ├── README.md ├── ShanghaiTech_Crowd_Counting_Dataset ├── part_A_final │ └── test_data │ │ ├── ground_truth │ │ └── GT_IMG_4.mat │ │ └── images │ │ └── IMG_4.jpg └── part_B_final │ └── test_data │ ├── ground_truth │ └── GT_IMG_4.mat │ └── images │ └── IMG_4.jpg ├── ShanghaitechWeights └── README.md ├── Visualization_ShanghaiTech.ipynb ├── Visualization_UCF-QNRF.ipynb ├── bayesian_preprocess_sh.py ├── datasets └── crowd.py ├── images ├── img_0030_heatpmap.png ├── img_0071.jpg ├── img_0071_heatpmap.png ├── img_0124_heatpmap.png ├── img_0208_heatpmap.png ├── img_0215_heatpmap.png ├── seg_img_0004_heatpmap.png ├── seg_img_0048_heatpmap.png ├── seg_img_0064_heatpmap.png ├── seg_img_0066_heatpmap.png └── seg_img_0323_heatpmap.png ├── losses ├── bay_loss.py └── post_prob.py ├── models ├── M_SFANet.py ├── M_SFANet_UCF_QNRF.py ├── M_SegNet.py ├── M_SegNet_UCF_QNRF.py ├── contextual_layer.py ├── lookahead.py └── modeling │ ├── __init__.py │ └── sync_batchnorm │ ├── __init__.py │ ├── batchnorm.py │ ├── comm.py │ ├── replicate.py │ └── unittest.py ├── test.py ├── train.py └── utils ├── helper.py ├── logger.py ├── regression_trainer.py └── trainer.py /Inference_speed.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting/HEAD/Inference_speed.ipynb -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting/HEAD/LICENSE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 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