Computer vision is a transformative technology that enables researchers to extract meaningful insights from visual data. In many fields, such as biology, medicine, environmental science, and engineering, visual data is abundant but underutilized. By learning computer vision, researchers can:
- Automate repetitive tasks, such as image classification, object detection, and segmentation.
- Analyze large datasets efficiently, enabling discoveries that would be impossible manually.
- Enhance the reproducibility and scalability of their research workflows.
- Apply cutting-edge techniques to solve domain-specific problems, such as disease diagnosis, ecological monitoring, or material analysis.
- Stay competitive in a research landscape increasingly driven by data and machine learning.
This workshop equips researchers with the skills to harness computer vision for their unique challenges, empowering them to innovate and accelerate their research.
| Lesson | Duration | Topics |
|---|---|---|
| 0: Setup & Foundations | 20 min | Jupyter setup, PyTorch import, GPU check |
| 1: Image Fundamentals | 30 min | Images as arrays, basic transforms, visualization |
| 2: Building Blocks of CNNs | 60 min | Convolutions, pooling, activation functions |
| 3: Classification Pipeline | 90 min | DataLoader, augmentation, training loop, validation, testing, inference |
| 4: Transfer Learning | 75 min | Pretrained models, fine-tuning, unfreezing layers, training on custom data |
| 5: Working with Real Data | 60 min | Data preparation, class imbalance, debugging common issues |
| 6: Hands-On Research Task | 60 min | Apply to provided research-adjacent dataset or bring-your-own |
| 7: Model Saving & Loading | 15 min | Save/load checkpoints and trained models |
| Q&A/Wrap-Up | 20 min | Open Q&A, troubleshooting, next steps |
- Understand the fundamentals of convolutional neural networks (CNNs) and how they process images.
- Load, preprocess, and augment image data for training.
- Train and evaluate image classification models.
- Use transfer learning effectively with pretrained models.
- Fine-tune models on custom datasets.
- Deploy basic computer vision models for inference.
- Diagnose and fix common training problems (e.g., overfitting, data imbalance).
- Document and reproduce experiments.
| Category | Tool | Purpose |
|---|---|---|
| Core Framework | PyTorch | Deep learning, model building |
| Vision Library | torchvision | Pretrained models, datasets, transforms |
| Supporting | NumPy, Pillow | Array operations, image I/O |
| Notebooks | Jupyter/Colab | Interactive learning environment |
| Visualization | Matplotlib, Tensorboard | Training curves, predictions, debugging |
| Experiment Tracking | Weights & Biases (W&B) OR MLflow | Reproducibility, hyperparameter logging |
| Optional Depth | OpenCV | Advanced preprocessing, domain-specific ops |
| Deployment | ONNX OR TorchScript | Model export and inference |