How Transfer Learning Works
Training an image classifier from scratch requires millions of labeled images and weeks of GPU time. Transfer learning skips most of that work. You start with a model (ResNet-50, EfficientNet-B4, or MobileNet-V3) that's already learned to recognize visual features from ImageNet's 14 million images. Then you fine-tune the last few layers on your specific images. This works with as few as 100-500 images per category and takes 30-60 minutes on a GPU.
The result is a model that understands your categories specifically. A model fine-tuned on product defect images knows the difference between a scratch, a dent, and a discoloration. A model fine-tuned on plant disease images distinguishes healthy leaves from bacterial spot, rust, and powdery mildew.
Training Pipeline
Organize your images into folders (one folder per category). The training script handles data augmentation (rotation, flipping, color jitter), train/validation splitting, learning rate scheduling, and early stopping. Training metrics (accuracy, loss, confusion matrix) are logged and viewable in TensorBoard. The best model checkpoint is saved automatically. The entire process is configured via a YAML file -- change the model architecture, learning rate, batch size, and augmentation settings without touching code.
Object Detection (Optional)
Beyond classification (what's in the image?), the system supports object detection (where is it in the image?) using YOLOv8. Detection draws bounding boxes around objects of interest and labels each box with a category and confidence score. Use this for counting objects, locating defects on a surface, or identifying multiple items in a scene. Training requires bounding box annotations, which you create using the included annotation tool.
REST API
The trained model serves predictions via a FastAPI server. Send an image and receive the predicted category, confidence score, and top-5 predictions in JSON format. Average response time is 50-100ms on GPU, 200-500ms on CPU. The API supports batch processing for bulk classification. Authentication and rate limiting are included for production deployment.