I made a computer vision tool for evaluating deadlift form
This is 1 of 200 launches in workout tracking and fitness coaching — see how it stacks up on momentum and crowding →
Details
- External ID
- 49924301
- Source
- HN
- Company
- —
- Product
- I made a computer vision tool for evaluating deadlift form
- Website domain
- github.com
- Launched
- Oct. 1, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.4090909090909091
- Tags
- —
- Fetched at
- Oct. 2, 2026, 5:01 p.m.
- Updated at
- Oct. 2, 2026, 5:01 p.m.
Description
I've shared a few deadlift demos in the past. This new demo includes some new models, accessed through the VLM Run Gateway:- SAM 3.1 to segment the barbell weight plate- ViTPose+ Large to measure the hip hinge angle, which serves as a backup for segmenting the reps. The pose data can definitely be used more later.- Gemma 4 26B through the Gateway's TypeSafe-compatible API to output the probability of the back being rounded or not. The predictions line up well with how I intentionally performed each rep.Of course, there are many caveats to what is good form, as it depends on the individual. All said, this has the pieces in place to quickly make adjustments to fit the individual better.In short, a tool like this gives people data to assess their form and improve over time.Let me know what you think!Demo video: https://www.youtube.com/watch?v=917J5oOvcBM
Enrichment
- Theme
- workout tracking and fitness coaching
- Vertical
- Healthcare
- Function
- Analytics & BI
- Audience
- B2C
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- computer vision tool for deadlift form evaluation
- Manually corrected
- False
Could you build this?
Yes The author explicitly wrapped existing computer vision foundation models (SAM and ViTPose+) via an existing hosted gateway API (VLM Run Gateway) to compute geometric angles and track reps.
Discussion
1 comment analyzed.
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Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.