Practical overview
When it fits
Use Waldo when you have CS2 footage with synchronized game audio and enough processed examples to train a model, and want to review model scores alongside the frames it examined.
Who it is for
- Counter-Strike 2 players who want to review gameplay clips for possible cheating behavior.
- People building a locally trained clip-analysis workflow from labeled CS2 footage.
Documented uses
- Extract two-second clips around kill moments detected from gameplay audio.
- Label processed clips and train or fine-tune a model locally.
- Score selected clips and inspect confidence values and the 16 frames analyzed.
- Save results locally and export them as JSON.
Requirements & limits
The project is documented as a local application with setup instructions for Windows 11 and Linux. Its README says internet is needed for initial setup, while analysis and result storage are local.
- A user-trained model is required; the README says at least 48 processed clips are needed to start training.
- Waldo detects kill moments through audio, so game audio must be present and synchronized; spectator kills and proximity headshot kills need manual removal.
- The README recommends 59.94 or 60p footage and lists substantial hardware requirements, including an NVIDIA GPU with CUDA support.
- The Linux installation script is explicitly described as untested; other GPU types may work in compatibility mode but are also untested.
- Scores are model confidence estimates for potential cheating, not conclusive findings.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare and review CS2 footage with Waldo
Process CS2 gameplay footage into clips, label clips, train a model, and review clip analysis results.
Before you start
- Waldo set up and accessible through its application workflow
- CS2 gameplay footage with game audio in sync; 59.94 or 60p footage is recommended
- At least 48 processed clips to start training
Steps
Upload raw CS2 footage; Waldo detects kill moments from audio and extracts two-second clips. Manually remove spectator kills and proximity headshot kills.
Label processed clips as “Cheater” or “Not Cheater,” then train a new model or fine-tune an existing one.
Select processed clips and a trained model for analysis; review confidence scores and the frame-by-frame view of the 16 analyzed frames.
Review the dashboard and individual clip results, and export results as JSON if needed.
Expected outcome
Waldo produces clip-level analysis results, including confidence information and frame-level details, which can be revisited and exported.
Guide limitations
- The workflow is specific to CS2 in the described version.
- At least 48 processed clips are needed to begin training.
- Spectator kills and proximity headshot kills must be removed manually.
- The README recommends 59.94 or 60p footage; it says other resolutions should work but does not guarantee other frame rates.
- The scores identify potential cheating and are not stated to be definitive determinations.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- Mr-Homeless/waldo · Official homepage title · retained in its source language