# Convolutional Architecture Exploration for Action Recognition and Image Classification

Source: [Convolutional Architecture Exploration for Action Recognition and Image Classification](<https://www.sota2.com/research/paper/68fa62451e209131ce9f86e7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:16:31.033Z

Authors: J.T. Turner, David Aha, Leslie Smith, Kalyan Moy Gupta

Published: 2015-12-23T14:54:43.000Z

## Abstract

Convolutional Architecture for Fast Feature Encoding (CAFFE) \[11\] is a software package for the training, classifying, and feature extraction of images. The UCF Sports Action dataset is a widely used machine learning dataset that has 200 videos taken in 720x480 resolution of 9 different sporting activities: diving, golf, swinging, kicking, lifting, horseback riding, running, skateboarding, swinging (various gymnastics), and walking. In this report we report on a caffe feature extraction pipeline of images taken from the videos of the UCF Sports Action dataset. A similar test was performed on overfeat, and results were inferior to caffe. This study is intended to explore the architecture and hyper parameters needed for effective static analysis of action in videos and classification over a variety of image datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1512.07502>)

[PDF](<https://arxiv.org/pdf/1512.07502.pdf>)