Dataset Configuration
Prepare and validate your image source path and annotation files.
Dataset Configuration Settings
Official Dataset Access Portals:
Workspace Status
Dataset Configuration Editor
Edit active dataset configurations (classes, paths) directly in the YAML file.
π Dataset Row Statistics & Technical Metadata
Comprehensive metrics on raw source rows vs loaded records, sensor modalities, and class breakdown.
Sensor & Capture Metadata
Storage & Format Specs
Class Object Frequencies (Loaded Rows)
Exabyte Ingest, Fusion & Stratification
Fuse Alpamayo and Waymo LiDAR/camera data with six-axis taxonomy stratification.
Automated 3D Perception (SAM + LiDAR)
Generate 2D SAM masks and lift to 3D bounding box proposals.
Temporal Tracking & Consistency
Kalman + Hungarian association for ID-smooth multi-frame tracks.
Quality Gate & Comparative Analytics
Benchmark against vendor GT: mAP, orientation error, ID swaps, process_units.
Launch Gate Validation
Verify safety thresholds before export is allowed.
Base Model Selection
YOLOv8 Nano (n)
Fastest, ultra-lightweight. Ideal for local testing and mobile/edge devices.
6.5 MBYOLOv8 Small (s)
Good trade-off between throughput and accuracy.
22.5 MBYOLOv8 Medium (m)
Recommended standard. Balanced accuracy/speed profile.
52.1 MBAlpamayo-R1-10B
Reasoning-focused autonomous driving model with Chain-of-Causation traces.
10B ParamsAlpamayo-1.5-10B
High-performance trajectory generation and lane-following control model.
10B ParamsExecution Device
NVIDIA DGX Spark Remote Cluster Setup:
Follow these commands to configure the libraries on your remote node:
# 1. Start Tailscale on the DGX node & login
sudo tailscale up
# 2. Setup python virtual environment and activate
python3 -m venv .venv
source .venv/bin/activate
# 3. Install PyTorch with CUDA 12 support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
# 4. Install Ultralytics and Hugging Face dependencies
pip install ultralytics huggingface_hub
# 5. Enable Ollama server host mapping for remote Gemma 4:
export OLLAMA_HOST=0.0.0.0
ollama run gemma4:26b
Training Controls
Real-time Output Logs & Curves
Waiting for training to start...
Auto-Labeler Parameters
Interactive Predictions Explorer
Quality Score Summary
Issue Diagnostics & Recommendations
AI Triage Copilot
Deep audit of the pipeline, data, and quality issues using Gemma 4 (26B)Multi-Camera Viewport
NuRec Telemetry & Kinematics
Human Bounding Box Triage
| ID | Class Label | Box Coordinates [x, y, w, h] | Conf | Action |
|---|
Chain-of-Causation (CoC) Reasoner
Gemma 4 Audit Critique:
Side-by-Side Annotation Boundary Comparators
Key Safety & Quality Metrics
SOTA Annotation Metrics Reference
| Annotation Model | Type | Latency (ms) | mAP [50] | Risk-Weighted Recall | Recall @ Critical Distance | VRU Recall (Pedestrians) | CoC Traces |
|---|
Export Deployment Engine
Serialize your trained model weights for production deployment and inference acceleration.
Finetuned Weights Details
IDE Model Context Protocol Servers
Enable or disable active plugins and external compute layers directly in mcp_config.json.
Raw config file: mcp_config.json
Path: ~/.gemini/config/mcp_config.json
California Statewide Conflict Map
Conflict Analysis Data Grid (FHWA SSAM)
| Street Interface β | County β | Type | Min TTC β | Min PET β | Max ΞS β | Severity β |
|---|