Computer Vision

Set up InternVL3 on your own hardware for document understanding tasks—OCR, tables, charts—with practical code and quantization options for consumer GPUs.

Detect, decode, and annotate barcodes and QR codes from images and webcams with Python and OpenCV

Build a color extraction pipeline that identifies dominant colors, matches named colors, and creates palette swatches from any image.

Detect layout changes, text edits, and added or removed sections between two document versions using OpenCV and PaddleOCR

Pull structured table data from PDFs and images using Table Transformer and OpenCV preprocessing

Detect and overlay driving lanes in video feeds with OpenCV classical methods and YOLOv8 segmentation models

Stitch images into panoramas with OpenCV’s high-level Stitcher and a manual pipeline using feature matching, homography, and blending.

Build an end-to-end defect detection system from labeled images to a REST API using YOLOv8 and FastAPI

Turn receipt photos into structured data with PaddleOCR, OpenCV preprocessing, and regex-based field extraction

Read text from real-world photos using PaddleOCR’s detection and recognition models with confidence filtering and batch processing

Count cars, trucks, and buses crossing a virtual line in video with YOLOv8 and centroid tracking

Build a frame interpolation pipeline that doubles or quadruples video FPS using the RIFE model

Track and remove objects from video frames using segmentation masks and temporal-aware inpainting.

Split videos into individual scenes automatically with PySceneDetect’s content and threshold detectors

Detect people and vehicles, monitor zones, count entries, and generate heatmaps from video feeds with Python

Detect any object in images using text prompts with Grounding DINO and zero training data

Create a production-ready visual inspection pipeline using PatchCore and OpenCV for manufacturing QA

Build a wildlife classifier that spots animals in camera trap photos and serves results over HTTP

Generate accurate image descriptions with BLIP models using a production-ready captioning pipeline in Python

Extract text from images and scanned documents using PaddleOCR and Tesseract with confidence scoring and batch processing

Turn photos of documents into clean, flat scans using OpenCV perspective warping in Python

Detect and classify hand gestures in real time with MediaPipe landmarks and a simple rule-based classifier

Create an automatic license plate reader with YOLO detection, OCR text extraction, and real-time video support in Python

Train a DenseNet-121 model to detect 14 chest X-ray pathologies and visualize predictions with Grad-CAM attention maps.

Track multiple objects across video frames by pairing YOLOv8 detections with DeepSORT identity matching

Track pixel-level motion between frames using RAFT optical flow from torchvision in a few lines of code

Detect faces in real time from webcam feeds using MediaPipe’s blazing-fast face detection models in Python.

Stream pixel-perfect segmentation masks to the browser in real time using SAM 2 and WebSocket connections

Segment every object in images and video frames using SAM 2 automatic masks, point prompts, and box prompts

Classify human actions in video clips and live streams using SlowFast dual-pathway networks in PyTorch

Process live video feeds with object detection, tracking, and zone-based analytics using Python and OpenCV

Learn to create powerful image Q&A systems that understand product photos, medical scans, and documents using pretrained vision-language models.

Build a document classification pipeline that sorts invoices, receipts, contracts, and more

Find defects and anomalies in images using deep learning without needing large labeled defect datasets

Turn low-resolution images into sharp high-res versions using neural network upscalers that add realistic detail

Detect, embed, and match faces in Python with InsightFace’s buffalo_l model and a few lines of code

Detect body landmarks, draw skeletons, and calculate joint angles with MediaPipe and a webcam

Search images by text or visual similarity using CLIP embeddings and a FAISS vector index

Load a Vision Transformer, preprocess images, run inference, and fine-tune on your own dataset

Set up YOLOv8 for image and video object detection with just a few lines of Python

Turn any photo into a depth map with Depth Anything V2 using three lines of Python or full manual control

Replace Tesseract with vision LLMs that read messy documents, handwriting, and tables accurately

Use SAM 2 to cut out objects from images with clicks or bounding boxes in a few lines of Python

Track and re-identify objects across video frames with ByteTrack, YOLO detection, and the supervision library

Step-by-step guide to training your own YOLO object detection model on custom data using the Ultralytics Python API and CLI.