Computer Vision Bootcamp with Python: YOLO, SAM & RF-DETR

Learn to run Yolo Nas for image and video inference, test Uranus and Yolo Nas object detection on webcam feeds, and export or fine-tune Nas models in Onnx format.

Perform object detection on images using Yolo Nas small with coco weights in a PyCharm project, leveraging Super Gradients and OpenCV to display bounding boxes and confidence scores.

Learn to perform object detection on videos using YOLO with COCO pretrained weights in Python and OpenCV, including setting up input and output directories and saving annotated output videos.

Run YOLO-NAS in Google Colab to perform object detection on images and videos, using pre-trained COCO weights, bounding boxes, and confidence scores.

Introduce uranus integration with deepsort for object tracking, test with pre-trained weights, then train on a custom dataset and assign unique IDs to tracked objects.

Implement object detection with YOLO-NAS and track objects using DeepSORT in PyCharm, configuring super gradients and OpenCV, processing videos, and preparing for a custom dataset.

Explore YOLO-NAS object detection and DeepSORT tracking on a custom vehicle dataset, generating unique IDs and bounding boxes to track vehicles across video frames.

Explore object detection on images and videos, and track each detected object with the sort algorithm. Build a project that counts vehicles entering and leaving.

Learn how to read and display images in Python with OpenCV using cv2.imread, cv2.imshow, and cv2.resize to fit the screen, including installing the OpenCV Python package.

Learn to read and display video with OpenCV in Python, from the first frame to full playback, via two scripts (part1.py and part2.py) that implement video capture, display, and resizing.

Learn to read, display, and save videos with OpenCV in Python, using VideoWriter to create an output file and test with live webcam capture in the next part.

Capture video from a camera with OpenCV in Python using the internal webcam. Save the live feed to a video file and preview YOLO-based object detection on images and videos.

Track objects in video with the SORT algorithm and YOLO-NAS detection, assigning a unique ID to each object as it enters and leaves the frame.

Demonstrates vehicles counting using YOLO-NAS for detection and SORT for tracking, counting entering (north) and leaving (south) by center points crossing defined lines.

Learn how to perform object tracking with YOLO-NAS on a custom ship dataset using the Sort algorithm, train on Roboflow data in Colab, and evaluate with mean average precision.

Develop a computer vision web app from scratch using Streamlit in Python, implementing real-time object detection with YOLO and a three-page interface for about, image detection, and video detection.

Learn to perform object detection on images with YOLO-NAS and visualize results in a Streamlit app, using image directories, bounding boxes, and adjustable confidence thresholds.

Build a Streamlit app for image object detection using YOLO-NAS, featuring an image uploader, confidence slider, and display of the output image.

Learn to implement object detection on videos and live webcam feeds with a Streamlit app, detailing frame processing, video upload, webcam toggle, and performance considerations.

Merge the object detection code for images and videos into deploy streamlit app.py, then deploy the app on streamlit.io by syncing a GitHub repo with requirements.txt and packages.txt.

Build a streamlit app that counts vehicles entering and leaving by combining object detection with a sort-based tracking algorithm, assigning unique IDs and counting line crossings via center coordinates.

Explore empty shelf detection using YOLO-NAS in a google colab workflow, detailing dataset preparation with roboflow, gpu training, evaluating with mean average precision, and video detections.

Detect license plates with YOLO-NAS object detection, then read the plate text using easy OCR, training on a license dataset with bounding boxes and a single class.

Learn to detect license plates with YOLO-NAS and read them with easy OCR, integrating model weights and video inputs for automatic number plate recognition.

Demonstrates license plate detection with yolo-nas and text recognition using paddleocr, building a Python project that processes videos with OpenCV and saves annotated outputs.

Learn how to perform multicam license plate detection and recognition with YOLO-NAS and OCR on multiple real-time video streams, including bounding box visualization and text extraction.

Build a face detection pipeline with yolo-nas in google colab gpu runtime, export data from roboflow, apply augmentation, train, and evaluate with mean average precision at 0.5.

Train and fine-tune a YOLO-NAS model on a PPE dataset to detect seven PPE classes, achieving about 80.8% mAP at 0.5 and testing on images and videos.

Create a Flask web app that uses YOLO-NAS with OpenCV to detect objects in images and videos.

Integrate a YOLO-NAS powered video into a Flask web app by organizing static assets, templating HTML, and rendering video output with front-end styles to show object detection results.

Learn to upload a video through a Flask web app, save it to the static folder, and run object detection on the uploaded file with settings and a submit control.

Develop and deploy a flask-based web app in Python that integrates YOLO-NAS for object detection across video, image, and live webcam feeds, while calculating frame width, height, and frame rate.

Design and implement the home page for a Flask web app that serves video, image, and live webcam detection using YOLO-NAS, wiring templates, routes, and assets.

Train a YOLO-NAS model on a personal protective equipment dataset in Google Colab with a GPU, covering dataset prep from Roboflow, training setup, and detections on images and videos.

Integrate a custom seven-class PPE model into a web app by saving weights, updating class names, and testing with images and videos through a Flask API and a front end.

Build an seo article writer app with a Streamlit web page and OpenAI ChatGPT integration. Generate academic articles with adjustable word count on object tracking, then download for publishing.

Train yolo-nas model on a 26-vegetable dataset, balance and augment training data to 19k images, achieving 66.44% mAP on test data, and prototype a streamlit app with chatgpt recipes.

Build a streamlit app using YOLO-NAS for vegetable detection in video, display results with OpenCV, and generate recipes via OpenAI ChatGPT on a recipe generator page.

Explore the segment anything model, a meta segmentation tool that identifies and extracts objects from images and videos, with zero-shot performance trained on 11 million images and 1.1 billion masks.

Learn to perform image segmentation with yolo-nas and the segment anything model, producing a person-specific mask from a multi-object output.