ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling

The official implementation of the paper ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling.

🤗 Try our models live! Check out the interactive demo on our HuggingFace Space.

Reults

Variants	Size (M)	GFLOPS (on HD)	SHA (MAE)	SHA (RMSE)	SHA (NAE, %)	SHB (MAE)	SHB (RMSE)	SHB (NAE, %)	QNRF (MAE)	QNRF (RMSE)	QNRF (NAE, %)	NWPU-Val (MAE)	NWPU-Val (RMSE)	NWPU-Val (NAE, %)	NWPU-Test (MAE)	NWPU-Test (RMSE)	NWPU-Test (NAE, %)
-P (Pico)	0.81	6.46	71.18	109.60	16.69	8.23	12.62	6.98	96.29	161.82	14.40	66.94	223.52	14.97	79.91	327.17	21.42
-N (Nano)	3.36	24.73	58.86	94.63	14.15	7.74	12.14	6.33	86.46	147.64	12.60	56.27	292.53	14.06	75.03	334.54	17.59
-T (Tiny)	10.53	61.39	56.36	86.09	13.26	6.67	9.90	5.52	76.02	129.40	11.10	46.74	145.32	11.31	66.43	323.27	13.45
-S (Small)	33.60	242.43	55.17	88.99	11.97	5.83	9.21	4.58	73.32	125.09	10.40	31.66	77.11	9.61	62.89	309.02	12.09
-B (Base)	105.60	800.99	47.81	75.04	11.06	5.51	8.63	4.48	69.46	121.88	10.18	28.26	64.84	9.20	60.09	298.95	10.44

Step 1: Install Dependencies

pip install -r requirements.txt

Step 2: Download Processed Datasets

• ShanghaiTech A: sha.zip
• ShanghaiTech B: shb.zip
• UCF-QNRF: qnrf.zip, qnrf.z01
• NWPU-Crowd: nwpu.zip, nwpu.z01, nwpu.z02, nwpu.z03, nwpu.z04, nwpu.z05, nwpu.z06, nwpu.z07, nwpu.z08

To unzip splitted .zip files, 7-Zip is recommended. You can use the following command to install 7-Zip and unzip the dataset:

sudo apt update
sudo apt install p7zip-full

7z x dataset.zip

Step 3: Run Training

Add the training code to run.sh and execute it:

sh run.sh

If you want to use the zero-inflated loss, set either --reg_loss or --aux_loss to zipnll. For example, you can set --reg_loss zipnll to use the zero-inflated loss for regression.

You can use an auxillary loss to improve the performance. For example, you might want to use the pre-defined multi-scale MAE loss by setting --aux_loss msmae and --scales 1 2 4.

The DMCount loss can also be used together with the zero-inflated loss. For example, you can set --reg_loss zipnll --aux_loss dmcount to use both losses.

Step 4: Test the Model

Use test.py or test.sh to test the model. You can specify the dataset, weight path, input size, and other parameters.

To generate the predicted counts on NWPU-Crowd Test, you need to use test_nwpu.py instead.

To visualize the results, use the notebooks/model.ipynb notebook.

Trained weights are also provided:

• ShanghaiTech A
• ShanghaiTech B
• UCF-QNRF
• NWPU-Crowd

Make sure to use the processed datasets and the exact commands pre-defined in test.sh to reproduce the same results.

Step 5: Visualize the Results

Use the notebooks/model.ipynb notebook to visualize the results.