| --- |
| language: en |
| tags: |
| - crowd-counting |
| - density-estimation |
| - computer-vision |
| - pytorch |
| - csrnet |
| - indian-metro |
| license: mit |
| --- |
| |
| # CSRNet Fine-tuned — Indian Metro Crowd Density Estimation |
|
|
| Fine-tuned CSRNet for real-time crowd density estimation on Indian metro platforms. |
|
|
| **MAE = 12.36** on held-out Indian metro test set. |
| 95.6% more accurate than YOLOv8 on the same data. |
|
|
| ## Model Details |
| - Architecture: CSRNet (VGG-16 frontend + dilated conv backend, dilation rate r=2) |
| - Training data: ShanghaiTech Part A + B (700 images) + Custom Indian Metro (88 images) |
| - Custom dataset: 5,030 head-point annotations across 111 images from Delhi Metro |
| (Rajiv Chowk), Hyderabad Metro (Ameerpet), and Mumbai Central |
| - Best epoch: 14 | MAE: 11.30 | MSE: 13.99 |
| - Inference: < 0.5 seconds on CPU |
|
|
| ## Results |
|
|
| | Approach | MAE | Density Map | Verdict | |
| |---|---|---|---| |
| | CNN Classifier (ResNet-18) | ~55 | No | Failed | |
| | YOLOv8 (pretrained COCO) | 283.23 | No | Failed | |
| | CSRNet Pretrained | ~50 | Yes | Baseline | |
| | **CSRNet Fine-tuned (this)** | **12.36** | Yes | Best | |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
| from model import CSRNet |
| |
| weights_path = hf_hub_download( |
| repo_id = "YOUR_HF_USERNAME/csrnet-indian-metro-crowd-density", |
| filename = "csrnet_v3_best.pth" |
| ) |
| |
| model = CSRNet() |
| model.load_state_dict(torch.load(weights_path, map_location="cpu")) |
| model.eval() |
| ``` |
|
|
| ## Author |
| Abdur Rahman Qasim — B.Tech CSE 2025–26 |
| Methodist College of Engineering and Technology, Hyderabad |
| Guide: Dr. Shivani Yadao |
