yuvrajengines/apextrack-track-condition-v2

ApexTrack AI — Track Condition Classifier V2

ApexTrack AI is a computer vision model for live racing track condition classification (dry, damp, wet). It powers dynamic strategy decision-support systems for race engineers and simulation platforms.

Model Overview

• Architecture: Vision Transformer (ViT-Base, google/vit-base-patch16-224-in21k)
• Task: 3-Class Image Classification (dry, damp, wet)
• Input Resolution: 224x224 RGB images
• Labels:
  • 0: damp
  • 1: dry
  • 2: wet

Dataset & Training

• Source Dataset: Weather Whiplash Surfaces (Real road & asphalt track surface conditions)
• Dataset Size: 190 original annotated images split into train (131), validation (27), and test (32).
• V2 Balancing: Controlled, conservative image augmentation was applied ONLY to the training split (bringing each training class to exactly 100 images = 300 total training images).
• Validation & Test Sets: Remained 100% untouched and unaugmented to guarantee zero data leakage and honest evaluation.

Performance Metrics (Evaluated on Untouched Test Set)

• Accuracy: 43.75%
• Macro F1: 40.78%
• Weighted F1: 42.51%

Per-Class Performance

Class	Precision	Recall	F1-Score
Dry	50.00%	72.73%	59.26%
Damp	22.22%	28.57%	25.00%
Wet	57.14%	28.57%	38.10%

Note on "Drying" Condition

"Drying" is not an image classification class. Instead, track drying is inferred temporally by the ApexTrack AI backend engine across sequential live predictions (e.g. wet → damp → dry).

Limitations & Disclaimer

• Prototype Status: This is an educational/hackathon prototype trained on a compact dataset.
• Decision Support: Predictions are designed for advisory decision support and should not be used in safety-critical autonomous control systems without human verification.