How to Build a Deep learning model to detect Ships

Mon, 19 Oct 2026 16:30:01 +0000

For anyone studying practical computer vision and deep learning deployment:
A frequent challenge in applied vision projects is balancing spatial accuracy against computational overhead during training. Selecting an architecture variant like YOLOv8l provides the parameter capacity necessary to resolve multi-scale objects in clustered environments, but success depends entirely on proper configuration and validation monitoring. This tutorial breaks down the mechanics of training a custom detector from scratch, detailing how loss convergence curves and validation checkpoints guide the selection of deployable weights.

The progression moves methodically from environment initialization to final model inference. It covers dataset re-indexing, parsing raw coordinate tags into standardized label files, executing supervised training runs while monitoring loss metrics, and implementing an OpenCV inference script to handle bounding box coordinates, class labels, and confidence thresholds on unseen imagery.

The Resources:

Reading on Medium: https://medium.com/object-detection-tutorials/yolov8-custom-object-detection-full-code-walkthrough-23fa476f834e

Detailed written explanation and source code: https://eranfeit.net/yolov8-custom-object-detection-full-code-walkthrough/

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Deep-dive video walkthrough: https://youtu.be/zAyPboTjcAA
This material is published solely for educational purposes. Feel free to leave constructive feedback or questions about threshold tuning and batch performance below.

How to build yolov8 dental object detection model

Fri, 21 Aug 2026 08:03:06 +0000

For anyone studying computer vision and medical image analysis, applying deep learning to dental radiography presents distinct challenges in feature extraction and anatomical localization.
Dental radiographs often feature dense, overlapping structures, low contrast margins, and significant variation across imaging hardware. Manual landmark identification remains labor-intensive and susceptible to inter-observer variability. This tutorial utilizes YOLOv8 as a single-stage detector because it delivers high mean Average Precision (mAP) on closely packed, low-contrast bounding boxes while maintaining real-time inference speeds suitable for practical clinical pipelines and batch processing.

Detailed written explanation and source code: https://eranfeit.net/how-to-build-yolov8-dental-object-detection-model/

The end-to-end workflow begins by establishing an isolated PyTorch environment with CUDA acceleration. The dataset is structured into normalized YOLO-format annotations paired with a custom dataset configuration file. Before initiating model training, normalized bounding box coordinates are converted to pixel dimensions and rendered using OpenCV to verify annotation alignment against raw X-ray data. Once validated, the YOLOv8 architecture is trained on GPU, and the resulting weights are evaluated by comparing predicted bounding boxes against ground-truth annotations in a side-by-side visual analysis.

Reading on Medium: https://medium.com/object-detection-tutorials/how-to-build-yolov8-dental-object-detection-model-07ee6ee36296

Join my Newsletter : https://eranfeit.net/advance-your-skills-in-computer-vision-ai/
Deep-dive video walkthrough: https://youtu.be/mfv1ps-tHDk

This content is intended for educational purposes only. Constructive feedback, technical questions, and discussions regarding custom dataset training or model optimization are welcome in the comments.

Coder Legion - Recent posts tagged objectdetection

How to Build a Deep learning model to detect Ships

How to build yolov8 dental object detection model