How to Train a Custom Object Detector with PyTorch Faster R‑CNN

The tutorial starts by explaining why a pre‑trained Faster R‑CNN model on MS COCO is useful and why transfer learning is needed to detect objects not present in COCO, such as microcontrollers. It introduces the Microcontroller Detection dataset from Kaggle, which contains 142 training and 7 validation images across four classes (Arduino, ESP8266, Raspberry Pi 3, Heltec ESP32 Lora) plus a background class.

Project structure is shown, including folders for data, source code, and outputs. The config.py file defines hyper‑parameters (batch size 4, image size 512, number of epochs 100, device selection, and paths for saving models and plots) and flags for visualizing transformed images.

Utility functions in utils.py include an Averager class for tracking loss averages, a collate_fn to handle variable‑size tensors, and data augmentation pipelines ( get_train_transform with flips, rotations, motion blur, median blur, and ToTensorV2; get_valid_transform only converts to tensors). A function show_tranformed_image visualizes transformed training samples when enabled.

Dataset implementation in datasets.py defines a MicrocontrollerDataset class that reads JPG images and corresponding XML annotations, extracts class names, parses bounding‑box coordinates, rescales them to the target size, and returns a dictionary with boxes, labels, area, iscrowd, and image_id. The dataset is wrapped in DataLoader objects for training and validation, with batch size and shuffling settings.

Model creation in model.py loads torchvision.models.detection.fasterrcnn_resnet50_fpn with pretrained weights, replaces the box predictor head using FastRCNNPredictor to match the five classes (background + four microcontroller types), and returns the modified model.

Training engine in engine.py imports configuration, the model, dataset loaders, and defines train and validate functions. Each iteration moves images and targets to the selected device, computes the loss dictionary, sums losses, records the loss value, updates the Averager, performs back‑propagation (training only), and updates a tqdm progress bar with the current loss. Validation runs under torch.no_grad() and records loss without gradient updates. The main loop runs for 100 epochs, prints epoch information, resets loss histories, measures epoch time, saves the model every SAVE_MODEL_EPOCH epochs, and saves loss plots every SAVE_PLOTS_EPOCH epochs using Matplotlib.

The provided console output shows the number of training (142) and validation (7) samples, download of the COCO weights, and loss values decreasing from ~0.5 to ~0.03 for training and from ~0.31 to ~0.04 for validation, indicating successful learning. Sample loss plots are generated.

Inference is performed in inference.py. The saved model model100.pth is loaded, images from ../test_data are pre‑processed (BGR→RGB, normalization, channel transpose), passed through the model, and detections with scores ≥ 0.8 are kept. Bounding boxes are drawn on the original image with class labels, and results are saved to ../test_predictions. Five test images (one per class, each containing two objects) are processed, and the predictions correctly detect all classes with tight bounding boxes.

In conclusion, the article demonstrates a complete workflow—from dataset preparation and augmentation to model modification, training, loss monitoring, and inference—for building a custom object detector using PyTorch Faster R‑CNN, and the results show accurate detection of microcontroller components.