It is a novel dataset for fine-grained object classification in videos. The CarVideos dataset contains over a million video frames annotated with bounding boxes around the visible cars as well as the specific year, make and model of each car. We implemented several state-of- the-art methods ...
Classification 数据详细介绍: CarEvaluationDataSet Abstract:Derivedfromsimplehierarchicaldecisionmodel,thisdatabasemaybeusefulfor testingconstructiveinductionandstructurediscoverymethods DataSet Characteristics: Multivariate Numberof Instances: 1728Area:N/A Attribute Characteristics: Categorical Numberof Attributes: 6 Date...
This repository gathers the code for car image classification from the in-class Kaggle challenge. See more details in report. Reproducing Submission Our model achieve 95.04% accuracy in testing set. To reproduct my submission without retrainig, do the following steps: Installation Ensemble Prediction ...
The task of car model classification is a challenging, fine-grained task and cars can have broad difference between different makes but at the same time subtle differences in comparison to cars within the same make. In addition, the pose of the car image samples in the dataset as well as ...
The Jupyter Notebook file contains method definitions for showcasing, class probability prediction and new image recognition. Also, a closer examination of the wrongly predicted cases is done to analyze which car brands need some dataset enrichment....
Car-Bike-Dataset(2 directories) chevron_right About this directory This is themain directorycontaining thesub directoriesasbike and car. folder Bike 2000 files folder Car 2000 files Input (108.34 MB) folder Data Sources arrow_drop_down Car vs Bike Classification Dataset ...
car-vs-bike-classification-dataset Language Python License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input3 files arrow_right_alt Output0 files arrow_right_alt Logs3.0 second run - successful arrow_right_alt Comments0 comments arrow_right_alt...
to form a strong classifier. The algorithm works by iteratively building decision trees based on the previous tree’s error to minimize the model’s overall error. Although it is widely used for classification, XGBoost can also be effectively used to predict continuous values in a regression task...
Here’s the parenting breakdown by dominant land use classification: Parents were under-represented in major shopping centres (I’m guessing a skew to younger employees), but also to a small extent universities and the central city. Parents were slightly over-represented in hospitals, Melbourne Air...
reinforcement-learningdeep-learningself-driving-cargtavdataset-generation UpdatedJan 14, 2020 C++ Timthony/self_drive Star1.1k Code Issues Pull requests 基于树莓派的自动驾驶小车,利用树莓派和tensorflow实现小车在赛道的自动驾驶。(Self-driving car based on raspberry pi(tensorflow)) ...