last update : 2025-07-31 This is my side project, and I’m using it to document the logs during the prototyping phase. I’m trying to build a simple and low-cost camera-based system to help automate the detection of lameness in cows. I’m trying to answer these questions :

  1. Using current open-source methods, what level of accuracy can be achieved for specific problem detection?
  2. What are the typical optimization strategies, and what is the ROI for each?
  3. Does a deep understanding of fundamental principles, like CNNs, lead to improved accuracy? What specific aspects benefit from this knowledge?

1 Logs & Plans

Week Phase Checkpoint Key Deliverables Acceptance Criteria
2 Data Annotation & Initial Model Training Completion of DLC labeling and initial YOLO Pose training 20–50 images annotated with DLC, initial YOLO Pose model trained Annotation error < 5 pixels;YOLO Pose mAP ≥ 70%
4 Lameness Detection Algorithm Development & Validation Implementation and initial video testing of lameness detection algorithm First version of lameness detection algorithm tested on videos Detection accuracy ≥ 70%;At least 10 video samples (5–8 seconds each) tested
6 Automated Data Processing Pipeline Setup Initial automation pipeline developed Automatic video capture → Upload to S3 → Automated processing & notification Pipeline success rate ≥ 90%;Processing delay ≤ 10 mins;Logging functionality enabled
8 System Integration & Final Validation Complete system test, optimization, and stability verification Comprehensive technical documentation and user manual completed Overall accuracy ≥ 75%;System stability ≥ 95%;Processing delay ≤ 10 mins;Clear and extensible documentation

1.1 Week 1~2 : Data Annotation & Initial Model Training

Our goal is :

  • Annotation error < 5 pixels;
  • YOLO Pose mAP ≥ 70%

tips :

logs :

Seq Date Do Cost Explain
1 07-28 2 weeks plan 2 hours
2 07-29 DLC training data use UI tools tag
3 07-29 change to yolo pose format yolo pose 11
4 07-29 yolo training yolo pose 11
5 07-31 format DLC->yolo script

FAQ

Q1 : How is the 70% mAP target determined?

The 70% mAP (mean Average Precision) target is a realistic and widely adopted benchmark for initial pose estimation model validation in the animal behavior analysis domain.

This target is based on:

  • Industry experience: Both academic literature and commercial implementations (e.g., CattleEye, Herd-i) show that with 20–50 well-annotated images, a YOLO Pose model typically achieves 65–75% mAP after initial training.
  • Model capability: YOLOv11-Pose is a lightweight yet powerful architecture. When trained on accurate DeepLabCut (DLC) annotations, it reliably produces reasonable results, even with small datasets.
  • Startup phase needs: A 70% mAP is high enough to demonstrate proof-of-concept validity but does not require exhaustive data labeling or model tuning, making it ideal for rapid iteration.

In summary, it’s a practical baseline for technical validation, not an upper limit.

Q2 : What is the potential for further improvement in model accuracy?

there is substantial room for improvement after the initial 70% baseline — especially by increasing data size and training diversity.

Effort Area Typical Improvement
Increasing dataset to 100–200 images +10–15% mAP (up to ~85%)
Better lighting and camera positioning +3–5% mAP
Model tuning and hyperparameter optimization +2–4% mAP
Advanced augmentations or backbone upgrades +2–5% mAP

Once the model reaches 85–90% mAP, further gains become harder and more expensive, due to:

  • Diminishing returns on annotation and training effort
  • Limited visual information quality from real-world farm conditions
  • Natural variance in animal behavior and environment

Above 90% mAP is possible but often not justified unless the application demands near-perfect precision. Commercial systems typically balance between performance and operational cost.

Q3 : What are the real barriers to commercialization

Reaching 80–85% mAP is technically feasible with existing tools, true commercialization challenges lie beyond model accuracy.
Real Barriers:

Barrier Description
Robustness & Generalization Adapting to different farm layouts, cow breeds, lighting, weather, and movement variability
System reliability Long-term stable operation with minimal maintenance in rough farm environments
Low-cost deployment Reducing the total cost of ownership: camera setup, compute hardware, and cloud storage
ROI justification Convincing farm operators that the system delivers tangible, timely financial benefits
UX and integration Easy-to-use interfaces, simple report generation, seamless farm system integration

To succeed as a startup, focus not on reinventing the algorithm, but on:

  1. Fast deployment, low maintenance : Plug-and-play camera kits, minimal calibration
  2. Cost-effective edge computing : Perform pose detection on edge devices to reduce cloud dependence
  3. Clear ROI for farmers : Simple dashboards showing actionable alerts (e.g., “This cow may be lame”), backed by historical comparison
  4. Data feedback loop : Build tools that allow farmers to confirm or correct detection, feeding back into the model to improve accuracy over time
  5. Modular architecture : Make your system easy to upgrade with better models, more sensors, or additional features later on

TODO : Q4 : some basic information about yolo

YOLOv11: An Overview of the Key Architectural Enhancements