This project implements Object Detection using Faster R-CNN with ResNet50-FPN, a state-of-the-art deep learning model for real-time object localization and classification. The model is fine-tuned on a custom dataset to accurately detect multiple object classes in images, combining region proposal networks (RPN) with a powerful ResNet-50 Feature Pyramid Network.
- Backbone: ResNet-50 with Feature Pyramid Network (FPN)
- Detector Head: Faster R-CNN
- Framework: PyTorch + Torchvision
- Loss Function: Classification + Bounding Box Regression
- Optimization: SGD / Adam with learning rate scheduler
- Dataset: Custom dataset prepared for object detection (COCO-style format)
- Classes: Multiple object categories (e.g., car, laptop, person, bicycle, etc.)
- Input Size: 224×224
- Data Split: 80% training / 20% validation
- Epochs: 10–15
- Batch Size: 4
- Hardware: CPU-compatible, GPU-accelerated optional
Training Pipeline:
from torchvision.models.detection import fasterrcnn_resnet50_fpn
model = fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = len(dataset.classes)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
- Convert model to ONNX or TorchScript for deployment
- Integrate real-time video detection
- Add custom UI for object annotation
- Experiment with MobileNet-FPN for faster inference
- PyTorch & Torchvision team for open-source detection models
- COCO Dataset for reference annotation format
- NVIDIA and Kaggle for providing GPU resources
Object Detection using Faster R-CNN (ResNet50-FPN) is powered by a robust deep learning stack — optimized for precision, scalability, and research-ready deployment.
🧠 Every library here plays a vital role — from feature extraction and region proposal to visualization and performance tracking.
🔗 Together, they enable an end-to-end detection pipeline that fuses computer vision and deep learning excellence.
HOSEN ARAFAT
Software Engineer, China
GitHub: https://github.com/arafathosense
Researcher: Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Image Processing