cvt
Datasets
All datasets matching “cvt”Road_Damage_Detection_USA
Road Damage Detection — YOLOv11 (US Roads)
Model Description
This model is a YOLOv11 object detection model that is meant to detect and identify road damage in images captured by cameras mounted to vehicles. Given an image, the model will output a bounding box and a label that shows the location and the type of damage there is.
Training approach: This model was fine tuned with pretrained weights on a subset of the Road Damage Detector dataset, using only images… See the full description on the dataset page: https://huggingface.co/datasets/cvtechniques/Road_Damage_Detection_USA.CV_train
Dataset Card for "CV_train"
More Information needed
CVTG-2KCVTG-2K is a challenging benchmark dataset comprising 2,000 prompts for complex visual text generation tasks. Generated via OpenAI's O1-mini API using Chain-of-Thought techniques, it features diverse scenes including street views, advertisements, and book covers. The dataset contains longer visual texts (averaging 8.10 words and 39.47 characters) and multiple text regions (2-5) per prompt. Half the dataset incorporates stylistic attributes (size, color, font), enhancing evaluation… See the full description on the dataset page: https://huggingface.co/datasets/dnkdnk/CVTG-2K.ParkingLotDetection
Model Description
Context
This YOLOv11 model aims to detect spaces in parking lots, whether filled or empty.
Training Approach
Fine-tuned from a YOLOv11 foundation model using Ultralytics framework. Combined from two public parking lot image datasets, standardized and augmented.
Intended Use Cases
Potential Use Cases:
Finding busy times and overall trends in parking lot traffic for urban design
Traffic monitoring apps
Parking lot owner monitoring
Training Data… See the full description on the dataset page: https://huggingface.co/datasets/cvtechniques/ParkingLotDetection.vehicle-damage-segmentation
Vehicle Damage Instance Segmentation
Model Description
Description: This YOLOv8-seg model is designed to automate vehicle insurance claims by isolating damage areas (Dents, Scratches, Broken Glass) with pixel-level accuracy.
Training Approach: Fine-tuned from a YOLOv8-seg foundation model using the Ultralytics framework.
Intended Use Case: Mobile app integration to allow claimants to get immediate repair estimates, significantly reducing manual inspection wait times.… See the full description on the dataset page: https://huggingface.co/datasets/cvtechniques/vehicle-damage-segmentation.DriftVision
🏎 Drift Car Tracking & Zone Analysis Model
📌 Overview
This project is a computer vision model designed to track drifting cars and quantify driver performance using aerial (drone) footage. The system detects and tracks vehicles during tandem runs and measures how they interact with predefined drift zones.
The current implementation is a proof of concept, developed specifically for footage from Evergreen Speedway in Monroe, Washington.
🧠 Model Description… See the full description on the dataset page: https://huggingface.co/datasets/cvtechniques/DriftVision.