harvitronix/five-video-classification-methods resource

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1182 stars · 459 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Companion code for a blog post demonstrating five deep learning approaches to video action classification using Keras and TensorFlow on the UCF101 dataset. It includes data extraction scripts, feature extraction, and training code for CNN, CNN+LSTM, LRCN, MLP, and 3D convolutional models.

Use cases

• classify human actions in videos with deep learning
• learn different video classification architectures in Keras
• train CNN-LSTM models on the UCF101 dataset
• compare 3D CNN vs frame-based video classification methods
• extract per-frame features from videos with a ConvNet
• reproduce video action recognition experiments

When to choose

• learning video classification techniques with worked examples
• comparing temporal modeling approaches (RNN, LRCN, 3D conv) on a standard dataset
• starting a UCF101-based research or class project

When to avoid

• production video classification systems needing modern architectures
• projects requiring recent TensorFlow/Keras versions
• needing a ready-made inference demo or pretrained models

Facets

learning-resource · maturity maintenance

machine-learning deep-learning video-processing image-processing machine-learning deep-learning computer-vision tutorials python cross-platform keras tensorflow video-classification action-recognition ucf101 cnn rnn lstm 3d-convolution educational-code video gpu linux

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