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--- |
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license: mit |
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model-index: |
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- name: ZIP-S |
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results: |
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- task: |
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type: crowd_counting |
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dataset: |
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name: ShanghaiTech A |
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type: shanghaitech_a |
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metrics: |
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- name: MAE |
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type: MAE |
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value: 55.17 |
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- name: RMSE |
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type: RMSE |
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value: 88.99 |
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- name: NAE |
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type: NAE |
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value: 0.1197 |
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- task: |
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type: crowd_counting |
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dataset: |
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name: ShanghaiTech B |
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type: shanghaitech_b |
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metrics: |
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- name: MAE |
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type: MAE |
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value: 5.83 |
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- name: RMSE |
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type: RMSE |
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value: 9.21 |
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- name: NAE |
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type: NAE |
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value: 0.0458 |
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- task: |
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type: crowd_counting |
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dataset: |
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name: UCF-QNRF |
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type: ucf_qnrf |
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metrics: |
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- name: MAE |
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type: MAE |
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value: 73.32 |
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- name: RMSE |
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type: RMSE |
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value: 125.09 |
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- name: NAE |
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type: NAE |
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value: 0.1040 |
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- task: |
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type: crowd_counting |
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dataset: |
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name: NWPU-Crowd-Val |
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type: nwpu_crowd_val |
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metrics: |
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- name: MAE |
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type: MAE |
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value: 31.66 |
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- name: RMSE |
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type: RMSE |
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value: 77.11 |
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- name: NAE |
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type: NAE |
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value: 0.0960 |
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- task: |
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type: crowd_counting |
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dataset: |
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name: NWPU-Crowd-Test |
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type: nwpu_crowd_test |
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metrics: |
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- name: MAE |
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type: MAE |
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value: 62.89 |
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- name: RMSE |
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type: RMSE |
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value: 309.02 |
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- name: NAE |
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type: NAE |
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value: 0.1209 |
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--- |
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# ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling |
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<!-- |
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[](https://paperswithcode.com/sota/crowd-counting-on-shanghaitech-a?p=ebc-zip-improving-blockwise-crowd-counting) |
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[](https://paperswithcode.com/sota/crowd-counting-on-shanghaitech-b?p=ebc-zip-improving-blockwise-crowd-counting) |
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[](https://paperswithcode.com/sota/crowd-counting-on-ucf-qnrf?p=ebc-zip-improving-blockwise-crowd-counting) |
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[](https://paperswithcode.com/sota/crowd-counting-on-nwpu-crowd-val?p=ebc-zip-improving-blockwise-crowd-counting) --> |
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<a href="https://www.buymeacoffee.com/yiming.ma" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a> |
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The official implementation of the paper [*ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling*](https://arxiv.org/pdf/2506.19955). |
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## Reults |
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| **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, %)** | |
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| ------------ | ------------ | ------------------ | ------------- | -------------- | ---------------- | ------------- | -------------- | ---------------- | -------------- | --------------- | ----------------- | ------------------ | ------------------- | --------------------- | ------------------- | -------------------- | ---------------------- | |
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| -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 | |
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| -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 | |
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| -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 | |
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| -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 | |
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| -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 | |
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## Step 1: Install Dependencies |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Step 2: Download Processed Datasets |
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- **ShanghaiTech A**: [sha.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/sha.zip) |
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- **ShanghaiTech B**: [shb.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/shb.zip) |
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- **UCF-QNRF**: [qnrf.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/qnrf.zip), [qnrf.z01](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/qnrf.z01) |
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- **NWPU-Crowd**: [nwpu.zip](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.zip), [nwpu.z01](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z01), [nwpu.z02](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z02), [nwpu.z03](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z03), [nwpu.z04](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z04), [nwpu.z05](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z05), [nwpu.z06](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z06), [nwpu.z07](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z07), [nwpu.z08](https://github.com/Yiming-M/EBC-ZIP/releases/download/dataset/nwpu.z08) |
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To unzip splitted `.zip` files, 7-Zip is recommended. You can use the following command to install 7-Zip and unzip the dataset: |
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```bash |
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sudo apt update |
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sudo apt install p7zip-full |
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7z x dataset.zip |
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``` |
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## Step 3: Run Training |
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Add the training code to `run.sh` and execute it: |
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```bash |
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sh run.sh |
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``` |
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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. |
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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`. |
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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. |
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## Step 4: Test the Model |
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Use `test.py` or `test.sh` to test the model. You can specify the dataset, weight path, input size, and other parameters. |
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To generate the predicted counts on NWPU-Crowd Test, you need to use `test_nwpu.py` instead. |
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To visualize the results, use the `notebooks/model.ipynb` notebook. |
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Trained weights are also provided: |
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- [**ShanghaiTech A**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_sha) |
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- [**ShanghaiTech B**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_shb) |
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- [**UCF-QNRF**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_qnrf) |
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- [**NWPU-Crowd**](https://github.com/Yiming-M/EBC-ZIP/releases/tag/weights_nwpu) |
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Make sure to use the processed datasets and the exact commands pre-defined in `test.sh` to reproduce the same results. |
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## Step 5: Visualize the Results |
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Use the `notebooks/model.ipynb` notebook to visualize the results. |
