Gears FOMO - Object detection

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
Akida FOMO (Faster Objects, More Objects) AkidaNet 0.50 Officially supported An object detection model based on AkidaNet (alpha=0.5, @224x224x3) designed to coarsely segment an image into a grid of background vs objects of interest.More info at: https://doc.brainchipinc.com/user_guide/akida_models.html#akidanet-training	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.

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Akida FOMO (Faster Objects, More Objects) AkidaNet 0.50
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

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Last training performance (validation set)

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