#convolutional neural network

Discover how to build a CNN model for skin melanoma classification using over 20,000 images of skin lesions

We'll begin by diving into data preparation, where we will organize, clean, and prepare the data form the classification model.

Next, we will walk you through the process of build and train convolutional neural network (CNN) model. We'll explain how to build the layers, and optimize the model.

Finally, we will test the model on a new fresh image and challenge our model.

Check out our tutorial here : https://youtu.be/RDgDVdLrmcs

Enjoy

Eran

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 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/

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.