YOLOv9 Alternatives

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.

YOLOv9 is an object detection model architecture released on February 21st, 2024.

Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.

YOLO-World is a zero-shot object detection model.

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.

Detectron2 is model zoo of it's own for computer vision models written in PyTorch.

You can retrieve bounding boxes whose edges match an angled object by training an oriented bounding boxes object detection model, such as YOLOv8's Oriented Bounding Boxes model.

LLaVA is an open source multimodal language model that you can use for visual question answering and has limited support for object detection.

Detection Transformer (DETR) is an end-to-end object detection model implemented using the Transformer architecture.

YOLOv7 is a state of the art object detection model.

YOLOX is a high-performance object detection model.

YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server.

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. PyTorch version.

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. Keras implementation.

MT-YOLOv6 is a YOLO based model released in 2022.

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.

YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8.

ByteTrack is a multi-object tracking computer vision algorithm.

DocTR is an Optical Character Recognition tool powered by deep learning.

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.

YOLOv5-OBB is a variant of YOLOv5 that supports oriented bounding boxes. This model is designed to yield predictions that better fit objects that are positioned at an angle.

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.

The 4M model is a versatile multimodal Transformer model developed by EPFL and Apple, capable of handling a handful of vision and language tasks.

L2CS-Net is a gaze estimation model that enables you to calculate where someone is looking and in what direction someone is looking.

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.

CoDet is an open vocabulary zero-shot object detection model.

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in Darknet.

A scalable, state of the art object detection model, implemented here within the TensorFlow 2 Object Detection API.

EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset

RTMDet is an efficient real-time object detector, with self-reported metrics outperforming the YOLO series. It achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, making it one of the fastest and most accurate object detectors available as of writing this post.

DINOv2 is a self-supervised method for training computer vision models developed by Meta Research and released in April 2023.

Kosmos-2 is a multimodal language model capable of object detection and grounding text in images.

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

OWL-ViT is a transformer-based object detection model developed by Google Research.

GPT-4 with Vision is a multimodal language model developed by OpenAI.

VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary