MMDetection

Collect high-resolution EL images.

Annotate defects in COCO format.

Use MMDetection with a Cascade R-CNN backbone for high precision.

Train with mixed-precision to handle large image patches.

Export to TensorRT for real-time inference on the factory floor.

Incorporate NuScenes or KITTI datasets.

Select a multi-scale detector like FCOS.

Configure the neck with FPN for varying object sizes.

Run distributed training on 8 GPUs.

Deploy via MMDeploy to NVIDIA Orin hardware.

Convert GeoTIFF data to manageable image tiles.

Apply instance segmentation (Mask R-CNN) to outline buildings.

Use Test-Time Augmentation (TTA) in MMDetection to increase accuracy.

Generate geo-referenced JSON output for GIS software.

Use a few-shot detection model from the MMDetection zoo.

Fine-tune on a small set of labeled product images.

Implement NMS (Non-Maximum Suppression) to handle high-density clusters.

Integrate output with a backend inventory database.

Pre-process DICOM images into 2D slices.

Train a Mask2Former model for high-fidelity segmentation.

Evaluate using Dice coefficient metrics available in MMDetection.

Deploy as a local microservice in a hospital network.

Aggregate low-light infrared images.

Utilize a robust backbone like ResNeXt-101.

Apply background subtraction techniques.

Run inference to generate timestamps and species counts for researchers.

Connect RTSP camera streams to a Python bridge.

Use a lightweight detector like YOLOX for high-speed processing.

Aggregate frame-by-frame detections into flow metrics.

Output results via JSON to a city management dashboard.