Analytics Dashboard
Model Accuracy
89.0%
+2.3%
vs previous periodTraining Time
90.0 hrs
-5.3%
vs previous periodInference Speed
0 ms
+0.0%
vs previous periodExperiment Success
20%
1 of 5 experiments completed
Model Performance Trends
Accuracy, training time, and inference speed over time
Performance Metrics
Last 30 days
Accuracy
Training Time
Inference Speed
Accuracy: 82.0%
Training: 120.0 hrs
Jan 1
Accuracy: 83.0%
Training: 115.0 hrs
Jan 15
Accuracy: 85.0%
Training: 105.0 hrs
Feb 1
Accuracy: 87.0%
Training: 95.0 hrs
Feb 15
Accuracy: 89.0%
Training: 90.0 hrs
Mar 1
Resource Utilization
CPU, memory, and GPU usage during training
Resource Metrics
Current training session
CPU Usage
78%Memory Usage
64%GPU Usage
92%Disk I/O
45%Dataset Distribution
Distribution of samples across datasets
Autonomous Navigation Dataset
12500 samples
Object Recognition Dataset
8750 samples
Robotic Arm Movements
5200 samples
Environmental Mapping
3800 samples
Human-Robot Interaction
2100 samples
Annotation Quality
Quality metrics for annotated data
Consistency Score87%
Precision92%
Recall84%
F1 Score88%
Training Progress
Current training progress by epoch
Current Epoch42 / 100
Loss0.0342
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Estimated Time Remaining
3h 24m