20 Exciting Open Source Computer Vision Projects for Beginners in 2025
Introduction
Computer vision has seen tremendous progress in the last decade and powers several real-world applications we use every day – from unlocking our smartphones with our face to autonomous driving. As an aspiring computer vision practitioner in 2024, you need a solid grasp of both the fundamental concepts as well as the latest techniques.
One of the best ways to achieve that is by working on open source projects. Open source computer vision projects provide a great avenue to go hands-on, learn collaboratively with the community, and build a strong portfolio. In this article, we will look at 20 such open source projects across various domains that are ideal for beginners to get started.
Why Work on Open Source Computer Vision Projects?
Here are some key benefits of working on open source computer vision projects:
- Apply theoretical concepts in practice and gain a deeper understanding
- Get exposed to real-world datasets and challenges
- Learn how to implement state-of-the-art models and techniques
- Collaborate with and learn from the vibrant open source community
- Contribute back to the community and showcase your skills
- Build a strong portfolio to land a computer vision role
20 Beginner-Friendly Open Source Computer Vision Projects
Let‘s dive into the list of 20 exciting open source computer vision projects for beginners, categorized by different application domains. We‘ll also look at the latest datasets and methods for each of them.
1. Image Classification Projects
Image classification is a fundamental computer vision task of categorizing images into one of several predefined classes. Here are a couple of datasets and projects to get started:
- CIFAR-100: An extension of the popular CIFAR-10 dataset with 100 classes and 600 images per class. Ideal for trying different CNN architectures.
- Stanford Cars: A dataset containing 16,185 images of 196 classes of cars, challenging due to different car poses, colors, etc. Use techniques like transfer learning.
2. Face Recognition Projects
Identifying and verifying people from their face images has many applications like surveillance, access control, etc. Check out these projects:
- VGGFace2: A large-scale face recognition dataset with 3.3 million images of 9000 identities. Implement face recognition models like FaceNet.
- DeepFace: Facebook‘s deep learning facial recognition system. Replicate the key components and results.
3. Neural Style Transfer Projects
Neural style transfer is a fascinating application of GANs where we mix the content of one image with the style of another. Try these:
- Fast Style Transfer: Implement the popular real-time style transfer method and generate creative images.
- Deep Photo Style Transfer: A newer approach to apply photo-realistic styles. Compare with the original neural style transfer.
4. Scene Text Detection Projects
Automatically detecting and reading text in natural scene images has applications in image search, automatic navigation, etc.
- ICDAR 2019: A robust word-level scene text detection dataset with 15000 images. Implement techniques like EAST, TextBoxes++.
- SVHN: The Street View House Numbers (SVHN) dataset contains over 600k digit images. Use it for number localization tasks.
5. Object Detection Projects
Localizing objects in an image with bounding boxes is a key perception task for applications like autonomous driving, surveillance, etc.
- Open Images V7: A huge dataset with 1.9M images and 16M bounding boxes across 600 categories. Use models like Faster R-CNN, YOLOv7.
- Objects365: A newer dataset for object detection with 365 categories and 10M bounding boxes. Compare with Open Images.
6. Semantic Segmentation Projects
Semantic segmentation involves labeling each pixel of an image with its object category. Useful for full scene understanding.
- ADE20k: Contains 20k images with pixel-wise labeling of 150 classes. Implement models like DeepLab, UPerNet.
- KITTI-360: A dataset for semantic segmentation of outdoor driving scenes with 100k frames. Pretrained models available.
7. Lane Detection for Self-Driving Cars
Detecting lane markings on the road is crucial for autonomous navigation. Try building lane detectors with these:
- CULane: Large dataset for lane detection with 55 hours of videos and 133K frames. Use techniques like LaneNet, SCNN.
- TuSimple: Another popular lane detection benchmark with 3626 video clips each 1 second long.
8. Image Captioning Projects
Automatically generating descriptive captions for images is a challenging vision+language task. Get started with:
- Flickr30k: An extension of Flickr8k with 30k images and 5 captions per image. Use encoder-decoder architectures.
- Conceptual Captions: A huge dataset with 3.3M image-caption pairs automatically collected from web data.
9. Human Pose Estimation Projects
Estimating the 2D/3D pose of humans from images/videos enables applications like activity recognition, motion capture, etc.
- COCO Keypoints: Contains 200k images with 250k instances labeled with 17 keypoints. Use OpenPose, HRNet models.
- Human3.6M: The largest 3D human pose dataset with 3.6 million images. Useful for 3D pose estimation.
10. Emotion Recognition Projects
Recognizing human emotions from facial expressions has applications in human-computer interaction, driver monitoring, etc.
- AffectNet: Contains 1M facial images labeled with 8 discrete emotions. Implement EmotionNet, ESR models.
- AFEW 8.0: A dynamic facial expressions dataset with 1809 videos clips labeled with 7 expressions.
These 10 are just the tip of the iceberg. There are many more interesting problems like action recognition, person re-identification, facial landmark detection, etc. that are worth exploring.
Tips to Learn Computer Vision
As you work on these open source projects, here are a few tips to maximize your learning:
- Start with a good understanding of the fundamentals like linear algebra, machine learning, deep learning, etc.
- Learn by doing. Don‘t just read theory, implement models on real datasets from scratch or using libraries.
- Try replicating results from latest research papers. This will greatly improve your understanding of state-of-the-art.
- Participate in online competitions on platforms like Kaggle, AICrowd to solve challenging problems and learn from others.
- Keep up with the latest developments by following top conferences like CVPR, ICCV, ECCV and researchers on social media.
Additional Learning Resources
Here are some great resources to learn computer vision in depth:
- Courses: Stanford CS231n, UMICH EECS 498, FastAI
- Books: Computer Vision: Algorithms and Applications by Szeliski, Computer Vision: Models, Learning and Inference by Prince
- Libraries: OpenCV, Scikit-Image for traditional CV; PyTorch, TensorFlow for Deep Learning based CV
Refer to these to supplement your learning from open source projects.
Conclusion
We looked at 20 exciting open source computer vision projects across various domains like image classification, face recognition, object detection, segmentation, etc. that are ideal for beginners to practice their skills on. Working on such projects, along with a good theoretical foundation, is the best way to master computer vision.
Pick a couple of projects that interest you and start hacking. Share your results and learnings with the community. You‘ll be amazed by how much you can learn and contribute within a short time. Keep the momentum going and become a proficient computer vision practitioner. Best of luck!
