synthetic_data_with_nvidia_replicator_and_edge_impulse_v2 - Object detection

Edge Impulse Experts / synthetic_data_with_nvidia_replicator_and_edge_impulse_v2 Public

Clone this project

v3_updated_data_random_90_epoch_2000

Primary version

Target: MacBook Pro 16" 2020 (Intel Core i9 2.4GHz)

Object Detection (Images) 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

Input layer (307,200 features)

Edge Impulse Experts / YOLOv5(added by Solomon Githu)

Output layer (3 classes)

Model

Model version:

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
YOLOv5 for Renesas DRP-AI Community Transfer learning model using YOLOv5 v5 branch with yolov5s.pt weights. This block is only compatible with Renesas DRP-AI.	Renesas
YOLOv5 Community Transfer learning model based on Ultralytics YOLOv5 using yolov5n.pt weights, supports RGB input at any resolution (square images only).	Community blocks
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
NVIDIA TAO RetinaNet Enterprise 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 Inc.
NVIDIA TAO YOLOV3 Enterprise 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 Inc.
NVIDIA TAO YOLOV4 Enterprise 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 Inc.
NVIDIA TAO SSD Enterprise 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 Inc.

These users will be notified over email when this job finishes.

	Name
	George Igwegbe

Object Detection (Images) settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

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

YOLOv5 for Renesas DRP-AI
Community

YOLOv5
Community

YOLOX for TI TDA4VM
Community

NVIDIA TAO RetinaNet
Enterprise

NVIDIA TAO YOLOV3
Enterprise

NVIDIA TAO YOLOV4
Enterprise

NVIDIA TAO SSD
Enterprise

Description

Last training performance (validation set)

Precision score

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

Personal

Enterprise

Public

Private

( of remaining)

Select a version

Object Detection (Images) settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Description

Last training performance (validation set)

Precision score

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

Settings