metadata
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
import torch
from huggingface_hub import hf_hub_download
from model import CSRNet
weights_path = hf_hub_download(
repo_id = "AbdurRahman011/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