Python enthusiastic

FaceFusion Face Swap Is WILD (Full FaceFusion Installation and Tutorial)

FaceFusion technology represents a significant shift in the accessibility of high-fidelity image and video synthesis. This tutorial provides a comprehensive guide to installing and utilizing FaceFusion for face swapping, focusing on the underlying architecture and the systematic workflow required to achieve seamless results.

The Insight :

The core technical challenge in face swapping lies in maintaining temporal consistency and lighting alignment across varying frames. FaceFusion addresses this by leveraging advanced deep learning models that decouple identity features from attribute features (such as expression and pose). This specific approach was chosen because it allows for high-resolution output without the extensive retraining typically required by older GAN-based architectures. By utilizing pre-trained models within a streamlined framework, developers can achieve professional-grade synthesis on consumer-grade hardware.

The Lesson :

The workflow begins with the environment configuration, ensuring that the necessary dependencies—including Python, FFmpeg, and CUDA for GPU acceleration—are correctly mapped. Once the environment is stable, the process moves from the selection of the source identity to the target medium. The logic behind the code centers on the "Processor" pipeline, where the software executes face detection, followed by the swapping algorithm, and finally, a restoration phase to enhance facial details. This modular sequence ensures that each step of the inference is optimized for both speed and visual fidelity.

Full tutorial  :

Detailed written explanation and source code here : https://eranfeit.net/facefusion-face-swap-is-wild-full-facefusion-installation-and-tutorial/

This content is provided for educational purposes only, intended to explore the capabilities of computer vision and AI synthesis. The community is invited to provide constructive feedback or ask technical questions regarding the installation process and model optimization. Eran Feit

Exploring Detectron2 For easy Object Detection

For anyone studying Computer Vision and Object Detection...

The core technical challenge this tutorial addresses is the complex configuration typically required to deploy Facebook (Meta) AI Research’s Detectron2 library. Unlike more "plug-and-play" frameworks, Detectron2 offers a highly modular architecture that can be intimidating for beginners due to its specific dependency on PyTorch and its unique configuration system. This approach was chosen to demonstrate how to leverage professional-grade research tools—specifically the Faster R-CNN R-101 FPN model—to achieve high-accuracy detection on the COCO dataset while maintaining the flexibility to run on standard CPU environments.

The workflow begins with establishing a clean, isolated Conda environment to manage dependencies like PyTorch and Ninja, followed by building Detectron2 from the source. The logic of the code follows a sequential pipeline: image ingestion and resizing via OpenCV to optimize memory usage, merging a pre-trained model configuration from the Detectron2 Model Zoo, and initializing a DefaultPredictor. The final phase involves running inference to extract prediction classes and bounding boxes, which are then rendered using the Visualizer utility to provide a clear, color-coded overlay of the detected objects.

Reading on Medium: https://medium.com/object-detection-tutorials/easy-detectron2-object-detection-tutorial-for-beginners-a7271485a54b

Detailed written explanation and source code: https://eranfeit.net/easy-detectron2-object-detection-tutorial-for-beginners/

Deep-dive video walkthrough: https://youtu.be/VKiYGmkmQMY

This content is for educational purposes only. The community is invited to provide constructive feedback or ask technical questions regarding the implementation or environment setup.

Eran Feit

Build an Object Detector using SSD MobileNet v3

For anyone studying object detection and lightweight model deployment...

The core technical challenge addressed in this tutorial is achieving a balance between inference speed and accuracy on hardware with limited computational power, such as standard laptops or edge devices. While high-parameter models often require dedicated GPUs, this tutorial explores why the SSD MobileNet v3 architecture is specifically chosen for CPU-based environments. By utilizing a Single Shot Detector (SSD) framework paired with a MobileNet v3 backbone—which leverages depthwise separable convolutions and squeeze-and-excitation blocks—it is possible to execute efficient, one-shot detection without the overhead of heavy deep learning frameworks.

The workflow begins with the initialization of the OpenCV DNN module, loading the pre-trained TensorFlow frozen graph and configuration files. A critical component discussed is the mapping of numeric class IDs to human-readable labels using the COCO dataset's 80 classes. The logic proceeds through preprocessing steps—including input resizing, scaling, and mean subtraction—to align the data with the model's training parameters. Finally, the tutorial demonstrates how to implement a detection loop that processes both static images and video streams, applying confidence thresholds to filter results and rendering bounding boxes for real-time visualization.

Reading on Medium: https://medium.com/@feitgemel/ssd-mobilenet-v3-object-detection-explained-for-beginners-b244e64486db

Deep-dive video walkthrough: https://youtu.be/e-tfaEK9sFs

Detailed written explanation and source code: https://eranfeit.net/ssd-mobilenet-v3-object-detection-explained-for-beginners/

This content is provided for educational purposes only. The community is invited to provide constructive feedback or ask technical questions regarding the implementation.

Eran Feit

For anyone studying  YOLOv8 Auto-Label Segmentation ,

The core technical challenge addressed in this tutorial is the significant time and resource bottleneck caused by manual data annotation in computer vision projects. Traditional labeling for segmentation tasks requires meticulous pixel-level mask creation, which is often unsustainable for large datasets. This approach utilizes the YOLOv8-seg model architecture—specifically the lightweight nano version (yolov8n-seg)—because it provides an optimal balance between inference speed and mask precision. By leveraging a pre-trained model to bootstrap the labeling process, developers can automatically generate high-quality segmentation masks and organized datasets, effectively transforming raw video footage into structured training data with minimal manual intervention.

The workflow begins with establishing a robust environment using Python, OpenCV, and the Ultralytics framework. The logic follows a systematic pipeline: initializing the pre-trained segmentation model, capturing video streams frame-by-frame, and performing real-time inference to detect object boundaries and bitmask polygons. Within the processing loop, an annotator draws the segmented regions and labels onto the frames, which are then programmatically sorted into class-specific directories. This automated organization ensures that every detected instance is saved as a labeled frame, facilitating rapid dataset expansion for future model fine-tuning.

Detailed written explanation and source code: https://eranfeit.net/boost-your-dataset-with-yolov8-auto-label-segmentation/

Deep-dive video walkthrough: https://youtu.be/tO20weL7gsg

Reading on Medium: https://medium.com/image-segmentation-tutorials/boost-your-dataset-with-yolov8-auto-label-segmentation-eb782002e0f4

This content is for educational purposes only. The community is invited to provide constructive feedback or ask technical questions regarding the implementation or optimization of this workflow.

Eran Feit

Real-Time Instance Segmentation using YOLOv8 and OpenCV

For anyone studying Dog Segmentation Magic: YOLOv8 for Images and Videos (with Code):

The primary technical challenge addressed in this tutorial is the transition from standard object detection—which merely identifies a bounding box—to instance segmentation, which requires pixel-level accuracy. YOLOv8 was selected for this implementation because it maintains high inference speeds while providing a sophisticated architecture for mask prediction. By utilizing a model pre-trained on the COCO dataset, we can leverage transfer learning to achieve precise boundaries for canine subjects without the computational overhead typically associated with heavy transformer-based segmentation models.

The workflow begins with environment configuration using Python and OpenCV, followed by the initialization of the YOLOv8 segmentation variant. The logic focuses on processing both static image data and sequential video frames, where the model performs simultaneous detection and mask generation. This approach ensures that the spatial relationship of the subject is preserved across various scales and orientations, demonstrating how real-time segmentation can be integrated into broader computer vision pipelines.

Reading on Medium: https://medium.com/image-segmentation-tutorials/fast-yolov8-dog-segmentation-tutorial-for-video-images-195203bca3b3

Detailed written explanation and source code: https://eranfeit.net/fast-yolov8-dog-segmentation-tutorial-for-video-images/

Deep-dive video walkthrough: https://youtu.be/eaHpGjFSFYE

This content is provided for educational purposes only. The community is invited to provide constructive feedback or post technical questions regarding the implementation details.

Eran Feit

Football Image segmentation using Yolov8

For anyone studying YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics...

The core technical challenge addressed in this tutorial is the limitation of traditional object detection in high-density sports environments. While standard bounding boxes can identify players, they often overlap in crowded scenes, making it difficult to extract precise metrics like exact pitch coverage or player-to-ball proximity. This tutorial utilizes the YOLOv8-seg architecture—specifically the Large variant (yolov8l-seg.pt)—to achieve pixel-level localization. This approach was chosen because instance segmentation provides the spatial context necessary to differentiate between overlapping athletes and fine pitch markings, which is a prerequisite for professional-grade tactical analysis.

The methodology follows a structured pipeline beginning with the preparation of multi-class datasets, including the conversion of JSON annotations into the YOLOv8 polygon-based TXT format. The process continues through environment configuration and model fine-tuning, where hyperparameters are adjusted to handle small, fast-moving objects like the football. The final stage involves an inference script that leverages OpenCV and NumPy to post-process raw model outputs into distinct, color-coded masks. This logical workflow ensures that the final data is structured for visualization and further analytical processing.

Reading on Medium: https://medium.com/@feitgemel/yolov8-segmentation-tutorial-for-multi-class-football-2b7996cda945

Detailed written explanation and source code: https://eranfeit.net/yolov8-segmentation-tutorial-for-multi-class-football/

Deep-dive video walkthrough: https://youtu.be/iXaq4OXNwSs

This content is for educational purposes only. The community is invited to provide constructive feedback or ask technical questions regarding the model architecture or implementation details.

Eran Feit

For anyone studying computer vision and semantic segmentation for environmental monitoring.

The primary technical challenge in implementing automated flood detection is often the disparity between available dataset formats and the specific requirements of modern architectures. While many public datasets provide ground truth as binary masks, models like YOLOv8 require precise polygonal coordinates for instance segmentation. This tutorial focuses on bridging that gap by using OpenCV to programmatically extract contours and normalize them into the YOLO format. The choice of the YOLOv8-Large segmentation model provides the necessary capacity to handle the complex, irregular boundaries characteristic of floodwaters in diverse terrains, ensuring a high level of spatial accuracy during the inference phase.

The workflow follows a structured pipeline designed for scalability. It begins with a preprocessing script that converts pixel-level binary masks into normalized polygon strings, effectively transforming static images into a training-ready dataset. Following a standard 80/20 data split, the model is trained with specific attention to the configuration of a single-class detection system. The final stage of the tutorial addresses post-processing, demonstrating how to extract individual predicted masks from the model output and aggregate them into a comprehensive final mask for visualization. This logic ensures that even if multiple water bodies are detected as separate instances, they are consolidated into a single representation of the flood zone.

Alternative reading on Medium: https://medium.com/@feitgemel/yolov8-segmentation-tutorial-for-real-flood-detection-963f0aaca0c3

Detailed written explanation and source code: https://eranfeit.net/yolov8-segmentation-tutorial-for-real-flood-detection/

Deep-dive video walkthrough: https://youtu.be/diZj_nPVLkE

This content is provided for educational purposes only. Members of the community are invited to provide constructive feedback or ask specific technical questions regarding the implementation of the preprocessing script or the training parameters used in this tutorial.

A quick Educational Walkthrough of YOLOv5 Segmentation

For anyone studying Quick Yolov5 segmentation tutorial in minutes ...

The transition from standard object detection to instance segmentation often involves navigating complex multi-stage architectures that can be difficult to implement and optimize. This tutorial utilizes the YOLOv5x-seg model to address this challenge, offering a single-pass framework that maintains high inference speeds while providing pixel-level precision. This specific approach is selected because it simplifies the workflow for developers who require detailed instance masks without the computational overhead or configuration complexity of more fragmented segmentation libraries.

The technical workflow begins with the establishment of a localized Conda environment to ensure dependency stability, particularly regarding the specific PyTorch and CUDA versions necessary for hardware acceleration. Following the configuration of the official repository and support libraries, the process moves into the execution phase using dedicated segmentation prediction scripts. This allows for the systematic processing of both individual assets and entire image directories, generating automated masks and visual overlays that are immediately ready for integration into larger computer vision pipelines.

Alternative reading on Medium: https://medium.com/@feitgemel/quick-yolov5-segmentation-tutorial-in-minutes-7b83a6a867e4

Detailed written explanation and source code: https://eranfeit.net/quick-yolov5-segmentation-tutorial-in-minutes/

Deep-dive video walkthrough: https://youtu.be/z3zPKpqw050?si=28h5BhkgfYpkfR_N

This content is provided for educational purposes only. The community is invited to provide constructive feedback or ask technical questions regarding the implementation details or environment configuration.

Build Custom Image Segmentation Model Using YOLOv8 and SAM

For anyone studying image segmentation and the Segment Anything Model (SAM), the following resources explain how to build a custom segmentation model by leveraging the strengths of YOLOv8 and SAM. The tutorial demonstrates how to generate high-quality masks and datasets efficiently, focusing on the practical integration of these two architectures for computer vision tasks.

Link to the post for Medium users : https://medium.com/image-segmentation-tutorials/segment-anything-tutorial-generate-yolov8-masks-fast-2e49d3598578

You can find more computer vision tutorials in my blog page : https://eranfeit.net/blog/

Video explanation: https://youtu.be/8cir9HkenEY

Written explanation with code: https://eranfeit.net/segment-anything-tutorial-generate-yolov8-masks-fast/

This content is for educational purposes only. Constructive feedback is welcome.

Eran Feit

Segment Anything with One mouse click

For anyone studying computer vision and image segmentation.

This tutorial explains how to utilize the Segment Anything Model (SAM) with the ViT-H architecture to generate segmentation masks from a single point of interaction. The demonstration includes setting up a mouse callback in OpenCV to capture coordinates and processing those inputs to produce multiple candidate masks with their respective quality scores.

Link to the post for Medium users : https://medium.com/image-segmentation-tutorials/one-click-segment-anything-in-python-sam-vit-h-bf6cf9160b61

You can find more computer vision tutorials in my blog page : https://eranfeit.net/blog/

Video explanation: https://youtu.be/kaMfuhp-TgM

Written explanation with code: https://eranfeit.net/one-click-segment-anything-in-python-sam-vit-h/

This content is intended for educational purposes only and I welcome any constructive feedback you may have.

Segment Custom Dataset without Training | Segment Anything

For anyone studying Segment Custom Dataset without Training using Segment Anything, this tutorial demonstrates how to generate high-quality image masks without building or training a new segmentation model. It covers how to use Segment Anything to segment objects directly from your images, why this approach is useful when you don’t have labels, and what the full mask-generation workflow looks like end to end.

Medium version (for readers who prefer Medium): https://medium.com/@feitgemel/segment-anything-python-no-training-image-masks-3785b8c4af78

Written explanation with code: https://eranfeit.net/segment-anything-python-no-training-image-masks/ Video explanation: https://youtu.be/8ZkKg9imOH8

This content is shared for educational purposes only, and constructive feedback or discussion is welcome.

For anyone studying Segment Anything (SAM) and automated mask generation in Python, this tutorial walks through loading the SAM ViT-H checkpoint, running SamAutomaticMaskGenerator to produce masks from a single image, and visualizing the results side-by-side. It also shows how to convert SAM’s output into Supervision detections, annotate masks on the original image, then sort masks by area (largest to smallest) and plot the full mask grid for analysis.

Medium version (for readers who prefer Medium): https://medium.com/image-segmentation-tutorials/segment-anything-tutorial-fast-auto-masks-in-python-c3f61555737e

Written explanation with code: https://eranfeit.net/segment-anything-tutorial-fast-auto-masks-in-python/ Video explanation: https://youtu.be/vmDs2d0CTFk?si=nvS4eJv5YfXbV5K7

For anyone studying Real Time Instance Segmentation using Detectron2, this tutorial shows a clean, beginner-friendly workflow for running instance segmentation inference with Detectron2 using a pretrained Mask R-CNN model from the official Model Zoo.

In the code, we load an image with OpenCV, resize it for faster processing, configure Detectron2 with the COCO-InstanceSegmentation mask_rcnn_R_50_FPN_3x checkpoint, and then run inference with DefaultPredictor. Finally, we visualize the predicted masks and classes using Detectron2’s Visualizer, display both the original and segmented result, and save the final segmented image to disk.

Video explanation: https://youtu.be/TDEsukREsDM

Link to the post for Medium users : https://medium.com/image-segmentation-tutorials/make-instance-segmentation-easy-with-detectron2-d25b20ef1b13

Written explanation with code: https://eranfeit.net/make-instance-segmentation-easy-with-detectron2/

Classify Agricultural Pests | Complete YOLOv8 Classification Tutorial

For anyone studying Image Classification Using YoloV8 Model on Custom dataset | classify Agricultural Pests

This tutorial walks through how to prepare an agricultural pests image dataset, structure it correctly for YOLOv8 classification, and then train a custom model from scratch. It also demonstrates how to run inference on new images and interpret the model outputs in a clear and practical way.

This tutorial composed of several parts :

🐍Create Conda enviroment and all the relevant Python libraries .

🔍 Download and prepare the data : We'll start by downloading the images, and preparing the dataset for the train

🛠️ Training : Run the train over our dataset

📊 Testing the Model: Once the model is trained, we'll show you how to test the model using a new and fresh image

Video explanation: https://youtu.be/--FPMF49Dpg

Link to the post for Medium users : https://medium.com/image-classification-tutorials/complete-yolov8-classification-tutorial-for-beginners-ad4944a7dc26

Written explanation with code: https://eranfeit.net/complete-yolov8-classification-tutorial-for-beginners/

This content is provided for educational purposes only. Constructive feedback and suggestions for improvement are welcome.

It explains how the dataset is organized, why YOLOv8-CLS is a good fit for this task, and demonstrates both the full training workflow and how to run predictions on new images.

This tutorial is composed of several parts :

🐍Create Conda environment and all the relevant Python libraries.

🔍 Download and prepare the data: We'll start by downloading the images, and preparing the dataset for the train

🛠️ Training: Run the train over our dataset

📊 Testing the Model: Once the model is trained, we'll show you how to test the model using a new and fresh image.

Video explanation: https://youtu.be/-QRVPDjfCYc?si=om4-e7PlQAfipee9

Link to the post with a code for Medium members : https://medium.com/image-classification-tutorials/yolov8-tutorial-build-a-car-image-classifier-42ce468854a2

Written explanation with code: https://eranfeit.net/yolov8-tutorial-build-a-car-image-classifier/

Animal Image Classification for Beginners

In this project a complete image classification pipeline is built using YOLOv5 and PyTorch, trained on the popular Animals-10 dataset from Kaggle.​

The goal is to help students and beginners understand every step: from raw images to a working model that can classify new animal photos.​

The workflow is split into clear steps so it is easy to follow:

Step 1 – Prepare the data: Split the dataset into train and validation folders, clean problematic images, and organize everything with simple Python and OpenCV code.​

Step 2 – Train the model: Use the YOLOv5 classification version to train a custom model on the animal images in a Conda environment on your own machine.​

Step 3 – Test the model: Evaluate how well the trained model recognizes the different animal classes on the validation set.​

Step 4 – Predict on new images: Load the trained weights, run inference on a new image, and show the prediction on the image itself.​

For anyone who prefers a step-by-step written guide, including all the Python code, screenshots, and explanations, there is a full tutorial here:

If you like learning from videos, you can also watch the full walkthrough on YouTube, where every step is demonstrated on screen:

Link for Medium users : https://medium.com/image-classification-tutorials/yolov5-image-classification-complete-tutorial-bd620e430ece

▶️ Video tutorial (YOLOv5 Animals Classification with PyTorch): https://youtu.be/xnzit-pAU4c?si=UD1VL4hgieRShhrG​

🔗 Complete YOLOv5 Image Classification Tutorial (with all code): https://eranfeit.net/yolov5-image-classification-complete-tutorial/​

If you are a student or beginner in Machine Learning or Computer Vision, this project is a friendly way to move from theory to practice.

VGG19 Transfer Learning Explained for Beginners

For anyone studying transfer learning and VGG19 for image classification, this tutorial walks through a complete example using an aircraft images dataset.

It explains why VGG19 is a suitable backbone for this task, how to adapt the final layers for a new set of aircraft classes, and demonstrates the full training and evaluation process step by step.

written explanation with code: https://eranfeit.net/vgg19-transfer-learning-explained-for-beginners/

video explanation: https://youtu.be/exaEeDfbFuI?si=C0o88kE-UvtLEhBn

Blog for Medium users : https://medium.com/image-classification-tutorials/vgg19-transfer-learning-explained-for-beginners-7f1164a14fc8

This material is for educational purposes only, and thoughtful, constructive feedback is welcome.