Video classification has become a quintessential task in the realm of machine learning, enabling systems to understand and categorize actions within moving images. In this guide, we will delve into using a 3D ResNet model with PyTorch for action classification in videos, trained on the Kinetics dataset, which comprises 400 action classes. With our user-friendly instructions, you’ll be able to set up your video classification environment and execute the code with ease!

Requirements

• PyTorch: Installation can be done via conda:

conda install pytorch torchvision cuda80 -c soumith

• FFmpeg and FFprobe for video processing:

wget http://johnvansickle.com/ffmpeg/release/ffmpeg-release-64bit-static.tar.xz
tar xvf ffmpeg-release-64bit-static.tar.xz
cd ffmpeg-3.3.3-64bit-static; sudo cp ffmpeg ffprobe /usr/local/bin;

• Python 3

Preparation

• Download the code repository.
• Download the pretrained model.
• In our experiments, the ResNeXt-101 architecture achieved the best performance (for further details, check the paper).

Usage

Assuming the input video files are located in the `.videos` directory, you can use the following commands to get started:

To Calculate Class Scores for Each 16 Frames

python main.py --input .input --video_root .videos --output .output.json --model .resnet-34-kinetics.pth --mode score

To Visualize the Classification Results

python generate_result_video.py

To Calculate Video Features for Each 16 Frames

python main.py --input .input --video_root .videos --output .output.json --model .resnet-34-kinetics.pth --mode feature

Understanding the Code: A Creative Analogy

Imagine you are a gourmet chef in a bustling kitchen. The 3D ResNet model serves as your well-trained sous-chef, equipped with all the skills necessary to identify the ingredients (video frames) and prepare a masterful dish (class predictions). As you input each video, your sous-chef meticulously analyzes every 16 frames, assessing the content and flavor (features) to accurately deliver a recipe card (classification results) outlining the dish’s components. You can choose either to simply know what dish you’ve prepared (class scores) or get the detailed recipe (video features).

Troubleshooting

If you encounter issues while setting up or running the code, consider these troubleshooting ideas:

• Ensure that all dependencies are correctly installed and updated.
• Check that your video files are in the correct directory and accessible.
• If there is a discrepancy with file paths, verify that they align with your directory structure.
• For additional help, consult the code repository where the README may have further insights.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.