Computer Vision Roadmap

A hands-on learning path with 15 end-to-end Computer Vision projects built with PyTorch Lightning. Each project covers a real-world task — from license plate recognition to CLIP-based image search — with training, evaluation, and inference code you can run locally.

Source repository: AppliedAI-Lab/Computer-Vision-Roadmap

What You Will Learn

Skill Area	Projects
Image Classification	Traffic signs, crop diseases, satellite imagery, medical X-rays, multi-label tagging
Object Detection & Localization	YOLO real-time detection, license plate localization
Segmentation	Medical CT segmentation (U-Net++, TransUNet)
OCR & Document AI	Handwritten text recognition (CRNN, TrOCR)
Face & Pose	Face recognition (FaceNet), human pose estimation
Vision + NLP	Image captioning, CLIP image-to-text search
Retrieval & Recommendation	Fashion visual similarity (DINOv2, OpenFashionCLIP)
Anomaly Detection	Industrial defect detection (PatchCore, EfficientAD)

Prerequisites

Requirement	Notes
Python	3.9+ recommended
PyTorch	CPU works for most projects; GPU strongly recommended for training
Git	To clone this repository
Kaggle account	Required for automatic dataset downloads (13 of 15 projects)
Tesseract OCR	Required only for Project 1

Optional but useful

CUDA-capable GPU — speeds up training significantly
TensorBoard — monitor training metrics (pip install tensorboard)
8 GB+ RAM — some datasets are large (e.g., crop disease ~87K images)

Quick Start

1. Clone the repository

git clone https://github.com/AppliedAI-Lab/Computer-Vision-Roadmap.git
cd Computer-Vision-Roadmap

2. Set up Kaggle API (one-time)

Most projects download datasets automatically via the Kaggle CLI.

Create a Kaggle account at kaggle.com
Go to Account → Create New Token to download kaggle.json
Place the token file:

# macOS / Linux
mkdir -p ~/.kaggle
mv ~/Downloads/kaggle.json ~/.kaggle/
chmod 600 ~/.kaggle/kaggle.json

# Windows
# Place kaggle.json at C:\Users\<YourUser>\.kaggle\kaggle.json

Install the Kaggle CLI:

pip install kaggle

3. Pick a project and run it

Each project lives in its own folder. The standard workflow is:

cd "1. License Plate Recognition System"   # pick any project folder

python -m venv .venv                      # optional but recommended
source .venv/bin/activate                 # macOS/Linux
# .venv\Scripts\activate                  # Windows

pip install -r requirements.txt
python utils/download_data.py             # download dataset (see notes below)
python train.py --config config.json      # train the model
python test.py --resume saved/models/.../best-checkpoint-....ckpt   # evaluate

Open TensorBoard during training:

tensorboard --logdir saved/log
# Then visit http://localhost:6006

Recommended Learning Path

Projects are numbered, but you can follow this progression based on difficulty and concept building:

Beginner ──────────────────────────────────────────────────────────────► Advanced

  3. Traffic Sign          4. Crop Disease         5. Satellite
     Classification            Classification           Classification
         │                        │                        │
         ▼                        ▼                        ▼
  1. License Plate         6. YOLO Detection       7. Face Recognition
         │                        │                        │
         ▼                        ▼                        ▼
  2. OCR / HTR             9. Pose Estimation      8. Image Captioning
         │                        │                        │
         ▼                        ▼                        ▼
 10. Medical Classify      11. Segmentation        12. Multi-Label
         │                        │                        │
         ▼                        ▼                        ▼
 14. Defect Detection      13. Fashion Rec.        15. CLIP Search

Level	Suggested Projects	Why Start Here
Beginner	3, 4, 5	Classic image classification with well-known datasets
Intermediate	1, 6, 7, 9	Detection, localization, and embedding-based tasks
Advanced	2, 8, 10, 11, 12	Transformers, medical imaging, multi-label, segmentation
Expert	13, 14, 15	CLIP/DINOv2 retrieval, anomaly detection, vision-language

Project Catalog

Each project has its own detailed README.md with models, metrics, and step-by-step instructions.

#	Project	CV Task	Key Models	Dataset
1	License Plate Recognition	Localization + OCR	ResNet18/34, Tesseract	Car Plate Detection
2	OCR + Document Understanding	Handwritten Text Recognition	CRNN, TrOCR	Handwriting Recognition (Kaggle)
3	Traffic Sign Recognition	Classification + STN	Simple CNN, STN-CNN	GTSRB
4	Crop Disease Detection	Multi-class Classification	MobileNetV2/V3, EfficientNet-B0	Plant Diseases
5	Satellite Image Classification	Land Cover Classification	ResNet18, MobileNetV3	Satellite Images
6	YOLO Object Detection	Real-Time Detection	YOLOv8, YOLOv10	COCO128
7	Face Recognition	Metric Learning	FaceNet	LFW / custom face dataset
8	Image Captioning	Vision + Language	CNN-RNN, ViT-GPT2	Flickr8k
9	Human Pose Estimation	Keypoint Detection	ViTPose, RTMPose	COCO Keypoints
10	Medical Image Classification	Binary Classification	ConvNeXt V2, MedMamba	Chest X-Ray Pneumonia
11	Medical Segmentation	Pixel-Level Segmentation	UNet++, TransUNet	SIIM Medical Images
12	Multi-Label Classification	Multi-Tag Prediction	ConvNeXt V2, Swin V2	Flickr30k
13	Fashion Recommendation	Visual Similarity / KNN	DINOv2, OpenFashionCLIP	Fashion Product Images
14	Industrial Defect Detection	Anomaly Detection	PatchCore, EfficientAD	MVTec AD
15	Image-to-Text Search	Vision-Language Retrieval	CLIP	Flickr8k

How to Run Any Project

Almost all projects (1–14) share the same structure and commands. Project 15 uses a slightly different entry point — see its README.

Standard workflow (Projects 1–14)

# 1. Enter the project folder
cd "<project folder name>"

# 2. Create and activate a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate        # macOS / Linux

# 3. Install dependencies
pip install -r requirements.txt

# 4. Download the dataset
python utils/download_data.py      # see exceptions below

# 5. Train
python train.py --config config.json

# 6. Monitor training (optional, in a separate terminal)
tensorboard --logdir saved/log

# 7. Evaluate with the best checkpoint
python test.py --resume saved/models/<ProjectName>/latest/best-checkpoint-epoch=XX-val_loss=X.XXX.ckpt

# 8. Run inference (if the project provides inference.py)
python inference.py --resume saved/models/.../best-checkpoint-....ckpt --image path/to/image.jpg

Project-specific notes

Project	Extra Steps	Download Command
1 License Plate	Install Tesseract OCR on your system	`python utils/download_data.py`
2 OCR	Dataset included or fetched during training setup — no separate download script	—
6 YOLO	Uses root-level download script	`python download_dataset.py`
12 Multi-Label	Run label preparation after download	`python utils/download_data.py` then `python utils/prepare_dataset.py`
13 Fashion	Download OpenFashionCLIP weights to `weights/openfashionclip.pt`	`python utils/download_data.py`
15 CLIP Search	Manual dataset placement; uses `main.py` instead of `train.py`	See Project 15 README

Project 15 — CLIP Image-to-Text Search

cd "15_Image-to-Text Search Engine (CLIP-based)"
pip install -r requirements.txt

# Manually download Flickr8k and place at:
#   data/Flickr8k_Dataset/captions.txt
#   data/Flickr8k_Dataset/images/

python main.py --model clip
python main.py --model clip --batch_size 32 --num_epochs 20 --lr 5e-5

Common Project Structure

Most projects follow this layout:

<Project Name>/
├── README.md              # Detailed docs, models, and results
├── config.json            # Hyperparameters and model selection
├── requirements.txt       # Python dependencies
├── train.py               # Training entry point
├── test.py                # Evaluation entry point
├── inference.py           # Real-time / single-image inference (some projects)
├── parse_config.py        # Config parser
├── base/                  # Base trainer, model, data loader classes
├── model/                 # Model architecture, loss, metrics
├── data_loader/           # Dataset and DataLoader
├── trainer/               # PyTorch Lightning trainer logic
├── logger/                # Logging and TensorBoard visualization
├── utils/                 # Helpers (download_data.py, etc.)
├── saved/
│   ├── models/            # Checkpoints (best-checkpoint-*.ckpt)
│   ├── log/               # TensorBoard logs
│   └── visual_results/    # Prediction visualizations
└── data/                  # Datasets (created after download)

Switching models

Many projects let you swap architectures by editing config.json — no code changes needed. Look for keys like "model_type", "arch", or "type" in each project's README.

Example (Project 6 — YOLO):

"model_type": "YOLOv8"

Change to "YOLOv10" to switch architectures.

Troubleshooting

Problem	Solution
`kaggle: command not found`	Run `pip install kaggle` and verify `kaggle.json` is in `~/.kaggle/`
Kaggle download fails (403)	Accept the dataset license on Kaggle's website before downloading
CUDA out of memory	Reduce `batch_size` in `config.json` or use CPU
Missing checkpoint on test	Train first, or use the path printed at the end of training
Tesseract not found (Project 1)	Install Tesseract and add it to your system PATH
Project 8 runs without real data	Project 8 auto-generates a small synthetic dataset if Flickr8k is missing

Tech Stack

Deep Learning: PyTorch, PyTorch Lightning
Model Libraries: timm, transformers, ultralytics, open-clip-torch
Computer Vision: OpenCV, Pillow, Albumentations
Medical Imaging: pydicom
Monitoring: TensorBoard
Datasets: Kaggle (primary source)

Contributing

Contributions are welcome! To add or improve a project:

Fork the repository
Create a feature branch
Add your changes with a clear README in the project folder
Open a pull request against main

Acknowledgments

Built by AppliedAI-Lab. Each project README contains paper references and dataset citations for the models and benchmarks used.

License

No license file is included in the repository. Contact the maintainers before using this code in commercial products.

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Computer Vision Roadmap

What You Will Learn

Prerequisites

Optional but useful

Quick Start

1. Clone the repository

2. Set up Kaggle API (one-time)

3. Pick a project and run it

Recommended Learning Path

Project Catalog

How to Run Any Project

Standard workflow (Projects 1–14)

Project-specific notes

Project 15 — CLIP Image-to-Text Search

Common Project Structure

Switching models

Troubleshooting

Tech Stack

Contributing

Acknowledgments

License

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README.md

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README.md

File metadata and controls

Computer Vision Roadmap

What You Will Learn

Prerequisites

Optional but useful

Quick Start

1. Clone the repository

2. Set up Kaggle API (one-time)

3. Pick a project and run it

Recommended Learning Path

Project Catalog

How to Run Any Project

Standard workflow (Projects 1–14)

Project-specific notes

Project 15 — CLIP Image-to-Text Search

Common Project Structure

Switching models

Troubleshooting

Tech Stack

Contributing

Acknowledgments

License