Now that you have built a complex object detection project using RCNN, it's time to explore more advanced topics in this domain. Object detection and deep learning are constantly evolving, and there are many exciting areas to explore. Here’s how you can continue your learning journey beyond this project.
Once you understand RCNN, you can explore more advanced architectures that improve speed, accuracy, and efficiency. Some of these include:
- Instead of using Selective Search, Faster RCNN introduces Region Proposal Networks (RPNs), making it significantly faster.
- Learn from PyTorch’s TorchVision Faster RCNN implementation:
📖 PyTorch Faster RCNN Tutorial
- YOLO predicts bounding boxes in a single forward pass, making it much faster than RCNN.
- Learn by implementing YOLOv8 from Ultralytics:
📖 YOLOv8 Documentation
- Traditional CNN-based models are being replaced by Vision Transformers (ViTs). DETR (DEtection TRansformer) by Meta is a transformer-based approach to object detection.
- Learn from the original DETR paper:
📖 End-to-End Object Detection with Transformers
- If you're interested in autonomous vehicles, try LiDAR-based 3D detection using models like PV-RCNN or PointNet++.
- Explore KITTI dataset and learn 3D point cloud processing:
📖 KITTI Dataset
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Deep Learning Specialization – Andrew Ng (Coursera)
- Covers CNNs, object detection, and advanced architectures.
- 📖 Link
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CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
- One of the best courses for deep learning in vision.
- 📖 Lecture Notes
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FastAI Practical Deep Learning
- Great for hands-on learners who prefer coding over theory.
- 📖 FastAI Course
- Keep up with state-of-the-art advancements by reading papers from arXiv and conferences like CVPR, NeurIPS, and ICCV.
- 📖 arXiv Computer Vision Papers
Joining a coding club or AI research group is one of the best ways to stay engaged, get feedback, and collaborate on projects.
¶ 🚀 Clubs and Communities
- Microsoft Innovation Hub (MIH) – If you are a member, leverage their resources for deep learning projects.
- IEEE Computer Society – Offers technical workshops and networking opportunities.
- OpenMMLab – Join the MMDetection community for advanced object detection frameworks.
- FastAI Discord & Forums – Great place for discussing ML topics.
- Kaggle – Participate in object detection competitions (COCO dataset challenges, etc.).
- Google Summer of Code (GSoC) – Work on AI open-source projects.
- ICCV & CVPR Challenges – Follow top-tier AI conferences for real-world competitions.
This codelabs discussed about object detection, implementation, GPU setup for AI tasks, From-scratch implementaion of R-CNN, real-time deployment of models like YOLO, advanced object detection topics, useful learning resources, and places to join AI communities. By continuously learning, collaborating, and experimenting, you can master deep learning and object detection.