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Computer Vision

What is Computer Vision?​

Exactly what it sounds like, vision for computers! CV is pattern recognition for pixels. By converting images & video into numbers, we let algorithms:

  • Detect objects (YOLO finding cars)
  • Classify scenes (ResNet deciding dog vs cat)
  • Segment boundaries (U-net tracing tumor margins)

Why CV wins hackathons​

Visual feedback = instant wow. Judges love bounding boxes and heatmaps because they see your progress. A pre-trained model + creative story often beats a perfect backend nobody sees. On top of that, CV is a field which has a lot of real-world impact, which is ideal for hackathons.

Pro-tip: rehearse the live demo twice-- once with Wi-Fi off--to avoid campus network surprises.

Core Tasks You Can Build Today​

TaskOne-linerStarter Model
Classification"What is in this image?"torchvision.models.resnet18
Object Detection"Where are the objects?"ultralytics YOLOv10
Segmentation"Which pixels belong together?"facebookresearch/segement-anything

Essential Tooling​

  • Ultralytics YOLO: A fast deep learning algorithm that scans the image once and makes predictions unlike older models. We will get to know more about it and work with this in the workshop!
  • OpenCV: A general-purpose CV library you're most likely going to use. There's a lot of image and video processing that goes on in a typical hackathon project such as resizing, rotating, color conversion, filters, etc. which can easily through OpenCV.
  • PyTorch: Perfect for Deep Learning frameworks for training and deploying CV models. You have access to multiple pre-trained CNN (Convolutional Neural Network) architectures including ResNet50, MobileNetV2, InceptionV3 and you can choose them depending on the size and quality of your dataset.
  • Google Colab: Free GPUs if your laptop protests.

Workshop Read Along​

  • Head over to the External Resources section and use the provided link to download the dataset.zip and data.yaml file.
  • Click on the Colab Notebook Link. In the colab notebook, upload the dataset.zip and the data.yaml files.
  • Run the first cell in the notebook. This will install the Ultralytics package, which contains the YOLO model. The installation may take a minute or two.
  • While Ultralytics is downloading, run the second cell. This will unzip the dataset. After extraction, you should see a train and val folder now. You can explore these folders to view the images or labels.
  • Once the library is installed and the dataset is extracted, run the third cell. This will start training your YOLO model.
  • After training the model for a certain amount of time, we can test it. The fourth cell loads the model and makes predictions on a test image by printing bounding boxes on empty and occupied spots.