Image Recognition

Start by specifying exactly what you want the model to recognize (e.g., objects, faces, text). Then gather a labeled dataset that represents the target classes with sufficient diversity and size. This step ensures the model learns the right features and avoids bias.

Why Prodigy: Prodigy supports object detection annotation workflows, which aligns with the need for labeling tools like LabelImg, Roboflow, or CVAT.

Resize all images to a consistent input size (e.g., 224x224), normalize pixel values, and apply data augmentation to improve generalization. This prepares raw images for the neural network.

Why OpenCV: OpenCV provides image preprocessing functions such as resizing, normalization, and augmentation, matching the need for tools like OpenCV, Albumentations, or torchvision.

Select a pre-trained CNN (e.g., ResNet, EfficientNet) and fine-tune it on your dataset. Train for enough epochs until validation loss stabilizes, using a GPU for speed.

Why PyTorch: PyTorch is a deep learning framework used for training and fine-tuning models, directly matching the need for PyTorch, TensorFlow, or GPU support.

Run the trained model on the test set to measure accuracy, precision, recall, and F1-score. Generate a confusion matrix to identify misclassifications. This confirms the model is ready for deployment.

Why scikit-learn: scikit-learn provides metrics and tools for model evaluation, matching the need for scikit-learn and matplotlib.

Export the trained model to a production format (e.g., ONNX, TensorRT) and set up an inference endpoint (e.g., Flask API, FastAPI). Test with sample images to ensure real-time or batch predictions work correctly.

If needed, apply model quantization or pruning to reduce latency and memory. Set up monitoring to track prediction drift and accuracy over time. This step ensures long-term reliability.