https://github.com/snigdho8869/computer-vision-projects

Explore diverse computer vision projects using Transfer Learning(TL), Convolutional Neural Networks (CNN), Autoencoder and more in this collaborative repository
https://github.com/snigdho8869/computer-vision-projects

autoencoder cnn cnn-classification cnn-model computer-vision convolutional-neural-networks deep-learning detection encoder-decoder-model image-classification image-processing inception-resnet-v2 inceptionv3 keras mobilenetv2 opencv recognition tensorflow transfer-learning yolov3

Last synced: 7 months ago
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Explore diverse computer vision projects using Transfer Learning(TL), Convolutional Neural Networks (CNN), Autoencoder and more in this collaborative repository

• Host: GitHub
• URL: https://github.com/snigdho8869/computer-vision-projects
• Owner: Snigdho8869
• Created: 2023-10-27T10:41:15.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-30T11:11:34.000Z (almost 2 years ago)
• Last Synced: 2024-01-25T18:46:57.575Z (over 1 year ago)
• Topics: autoencoder, cnn, cnn-classification, cnn-model, computer-vision, convolutional-neural-networks, deep-learning, detection, encoder-decoder-model, image-classification, image-processing, inception-resnet-v2, inceptionv3, keras, mobilenetv2, opencv, recognition, tensorflow, transfer-learning, yolov3
• Language: Jupyter Notebook
• Homepage:
• Size: 32.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Computer Vision Projects

This repository hosts a collection of computer vision projects using deep learning techniques, focusing on various real-world applications. Each project is designed to demonstrate the power of transfer learning and convolutional neural networks (CNN) in solving practical problems. Here's what you'll find in this repository:

## Project Highlights:

- **Cataract Detection:**

  Detect cataracts in eye images using pre-trained models and fine-tuning.

- **Traffic Sign Detection:**

Identify and classify traffic signs from images for improved road safety.

- **Pneumonia Detection:**

Utilize deep learning to diagnose pneumonia from chest X-ray images.

- **Emotion Detection:**

Build models to recognize and classify emotions from facial expressions.

- **MNIST Digit Classification:**

Develop a model to classify handwritten digits from the MNIST dataset, a fundamental task for beginners in computer vision.

- **Driver Drowsiness Detection:**

  Enhance road safety with Driver Drowsiness Detection! Utilize Transfer Learning and Convolutional Neural Networks with Parallel Convolution Architecture to identify and classify driver drowsiness.

- **Eye Diseases:**

    Contribute to healthcare with a deep learning project focused on classifying eye diseases. Employ Convolutional Neural Networks (CNNs), including those with a Parallel Convolution Architecture, for accurate disease classification.

- **Lane Detection for Autonomous Vehicles:**

    Contribute to the development of autonomous vehicles with a lane detection algorithm using computer vision techniques. Highlight detected lanes on the road, providing a visual representation crucial for vehicle navigation and safety.

- **Object Detection Using YOLOv3:**

  Experience the speed and accuracy of the YOLOv3 algorithm for real-time object detection. This repository provides code for implementing object detection and showcases the versatility of YOLOv3 in identifying and tracking various objects.