RPi-blues-yolo - Object detection

Edge Impulse Experts / RPi-blues-yolo Public

Target: Cortex-M4F 80MHz Clone this project

Object detection settings

Training settings

Number of training cycles

Please provide a valid number of training cycles (numeric only)

Learning rate

Please provide a valid number for the learning rate (between 0 and 1)

Training processor

Please provide a valid training processor option

Advanced training settings

Validation set size

Please provide a valid number for the train/validate split (between 0 and 1)

Split train/validation set on metadata key

Batch size

Profile int8 model

Neural network architecture

Save

Input layer (307,200 features)

Edge Impulse Experts / YOLOv5(added by Solomon Githu)

Output layer (2 classes)

Model

Model version:

Last training performance (validation set)

Precision score

98.3%

On-device performance

Engine:

Inferencing time

N/A

Peak RAM usage

0.0K

Flash usage

3.5M

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	Name
	Justin Lutz

Object detection settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Precision score

Confusion matrix (validation set)

Data explorer (full training set)

On-device performance

Inferencing time

Peak RAM usage

Flash usage

Object detection settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Precision score

Confusion matrix (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage