Car Detection and Tracking - Object detection

Edge Impulse Experts / Car Detection and Tracking Public

Target: Raspberry Pi 4 Clone this project

Neural Network settings

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sys.path.append('./resources/libraries')
 
 
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Input layer (307,200 features)

MobileNetV2 SSD FPN-Lite 320x320

Output layer (1 classes)

Model

Model version:

Last training performance (validation set)

Precision score

87.0%

Metrics (validation set)

Metric	Value
mAP	0.56
mAP@[IoU=50]	0.80
mAP@[IoU=75]	0.64
mAP@[area=small]	0.28
mAP@[area=medium]	0.69
mAP@[area=large]	0.91
Recall@[max_detections=1]	0.21
Recall@[max_detections=10]	0.64
Recall@[max_detections=100]	0.64
Recall@[area=small]	0.38
Recall@[area=medium]	0.76
Recall@[area=large]	0.93

Data explorer (full training set)

Retrain this model to enable the feature explorer.

On-device performance

Engine:

Inferencing time

386 ms.

Peak RAM usage

0.0K

Flash usage

11.0M

This model won't run on MCUs. Calculated arena size is >6MB

Did you know? You can customize your model through the Expert view (click on to switch),
or can even bring your own model (in PyTorch, Keras or scikit-learn).

Model	Author
MobileNetV2 SSD FPN-Lite (320x320 only) Officially supported A pre-trained object detection model designed to locate up to 10 objects within an image, outputting a bounding box for each object detected. The model is around 3.7MB in size. It supports an RGB input at 320x320px. For other resolutions, use FOMO or an NVIDIA TAO model.	Edge Impulse
FOMO (Faster Objects, More Objects) MobileNetV2 0.1 Officially supported An object detection model based on MobileNetV2 (alpha 0.1) designed to coarsely segment an image into a grid of background vs objects of interest. These models are designed to be <100KB in size and support a grayscale or RGB input at any resolution.	Edge Impulse
FOMO (Faster Objects, More Objects) MobileNetV2 0.35 Officially supported An object detection model based on MobileNetV2 (alpha 0.35) designed to coarsely segment an image into a grid of background vs objects of interest. These models are designed to be <100KB in size and support a grayscale or RGB input at any resolution.	Edge Impulse
YOLOv5(added by Solomon Githu) Enterprise Object Detection using YOLOv5	Edge Impulse Experts
YOLOx Enterprise YOLOx fork for TI TDA4VM	Edge Impulse Experts
YOLOX for TI TDA4VM Community TI's EDGEAI YOLOX. https://github.com/TexasInstruments/edgeai-yolox. Outputs ONNX v7 model format both with and without final detect layers using PyTorch 1.7.1. See the implementation https://github.com/edgeimpulse/example-custom-ml-block-ti-yolox/tree/onnx-v7	Texas Instruments

The backbone is the foundational element of a neural network model, responsible for extracting meaningful features from input data, enabling subsequent parts to perform anomaly scoring or classification.

Description	Author
EfficientNet V2B0 Uses around 1379K RAM based on your input size, and between 240-1675K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.35 Uses around 1217K RAM based on your input size, and between 69-134K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.50 Uses around 1221K RAM based on your input size, and between 78-168K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.75 Uses around 2421K RAM based on your input size, and between 109-266K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 1.0 Uses around 2431K RAM based on your input size, and between 126-372K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 Uses around NaNK RAM based on your input size, and 58K ROM, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse

The scoring function operates on the features extracted by the backbone, and returns anomaly scores for each segment of the image.

Description	Author	Recommended
MobileNetV2 SSD FPN-Lite (320x320 only) A pre-trained object detection model designed to locate up to 10 objects within an image, outputting a bounding box for each object detected. The model is around 3.7MB in size. It supports an RGB input at 320x320px. For other resolutions, use FOMO or an NVIDIA TAO model.	Edge Impulse
FOMO (Faster Objects, More Objects) MobileNetV2 0.1 An object detection model based on MobileNetV2 (alpha 0.1) designed to coarsely segment an image into a grid of background vs objects of interest. These models are designed to be <100KB in size and support a grayscale or RGB input at any resolution.	Edge Impulse
FOMO (Faster Objects, More Objects) MobileNetV2 0.35 An object detection model based on MobileNetV2 (alpha 0.35) designed to coarsely segment an image into a grid of background vs objects of interest. These models are designed to be <100KB in size and support a grayscale or RGB input at any resolution.	Edge Impulse
YOLOv5(added by Solomon Githu) Object Detection using YOLOv5	Edge Impulse
YOLOx YOLOx fork for TI TDA4VM	Edge Impulse
YOLOv5 for Renesas DRP-AI Transfer learning model using YOLOv5 v5 branch with yolov5s.pt weights. This block is only compatible with Renesas DRP-AI.	Edge Impulse
YOLOv5 Transfer learning model based on Ultralytics YOLOv5 using yolov5n.pt weights, supports RGB input at any resolution (square images only).	Edge Impulse
YOLOX for TI TDA4VM TI's EDGEAI YOLOX. https://github.com/TexasInstruments/edgeai-yolox. Outputs ONNX v7 model format both with and without final detect layers using PyTorch 1.7.1. See the implementation https://github.com/edgeimpulse/example-custom-ml-block-ti-yolox/tree/onnx-v7	Edge Impulse
NVIDIA TAO RetinaNet Object detection model with superior performance on smaller objects. Configurable backbones optimized for targets from MCU to GPU. Supports rectangular input. Image width and height must be multiples of 32. Training requires GPU.	Edge Impulse
NVIDIA TAO YOLOV3 Object detection model that is fast and accurate. Configurable backbones optimized for targets from MCU to GPU. Supports rectangular input. Image width and height must be multiples of 32. Training requires GPU.	Edge Impulse
NVIDIA TAO YOLOV4 Object detection model that is fast and accurate. Configurable backbones optimized for targets from MCU to GPU. Supports rectangular input. Image width and height must be multiples of 32. Training requires GPU.	Edge Impulse
NVIDIA TAO SSD Object detection model for general purpose use. Configurable backbones optimized for targets from MCU to GPU. Supports rectangular input. Image width and height must be multiples of 32. Training requires GPU.	Edge Impulse

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	Name
	Naveen

Neural Network settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Precision score

Confusion matrix (validation set)

Metrics (validation set)

Data explorer (full training set)

On-device performance

Inferencing time

Peak RAM usage

Flash usage

MobileNetV2 SSD FPN-Lite (320x320 only)
Officially supported

FOMO (Faster Objects, More Objects) MobileNetV2 0.1
Officially supported

FOMO (Faster Objects, More Objects) MobileNetV2 0.35
Officially supported

YOLOv5(added by Solomon Githu)
Enterprise

YOLOx
Enterprise

YOLOX for TI TDA4VM
Community

EfficientNet V2B0

MobileNetV2 0.35

MobileNetV2 0.50

MobileNetV2 0.75

MobileNetV2 1.0

MobileNetV2 0.1

MobileNetV2 SSD FPN-Lite (320x320 only)

FOMO (Faster Objects, More Objects) MobileNetV2 0.1

FOMO (Faster Objects, More Objects) MobileNetV2 0.35

YOLOv5(added by Solomon Githu)

YOLOx

YOLOv5 for Renesas DRP-AI

YOLOv5

YOLOX for TI TDA4VM

NVIDIA TAO RetinaNet

NVIDIA TAO YOLOV3

NVIDIA TAO YOLOV4

NVIDIA TAO SSD

Neural Network settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Precision score

Confusion matrix (validation set)

Metrics (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage