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YOLOv5 Alternatives
Explore alternatives to the YOLOv5 object detection model.
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Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using
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YOLOv8
YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.
Object Detection
Deploy with Roboflow
YOLOv9
YOLOv9 is an object detection model architecture released on February 21st, 2024.
Object Detection
Deploy with Roboflow
GroundingDINO
Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.
Object Detection
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YOLO-World
YOLO-World is a zero-shot object detection model.
Object Detection
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YOLOv5
A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.
Object Detection
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YOLO11
YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.
Object Detection
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Detectron2
Detectron2 is model zoo of it's own for computer vision models written in PyTorch.
Object Detection
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MediaPipe
Object Detection
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YOLOv8 Oriented Bounding Boxes
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.
Object Detection
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LLaVA-1.5
LLaVA is an open source multimodal language model that you can use for visual question answering and has limited support for object detection.
Object Detection
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DETR
Detection Transformer (DETR) is an end-to-end object detection model implemented using the Transformer architecture.
Object Detection
Deploy with Roboflow
YOLOv7
YOLOv7 is a state of the art object detection model.
Object Detection
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YOLOX
YOLOX is a high-performance object detection model.
Object Detection
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YOLOv10
YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".
Object Detection
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Faster R-CNN
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.
Object Detection
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YOLOv3 PyTorch
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.
Object Detection
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YOLOv3 Keras
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.
Object Detection
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MT-YOLOv6
MT-YOLOv6 is a YOLO based model released in 2022.
Object Detection
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YOLOv4 PyTorch
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.
Object Detection
Deploy with Roboflow
YOLO-NAS
YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8.
Object Detection
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ByteTrack
ByteTrack is a multi-object tracking computer vision algorithm.
Object Detection
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DocTR
DocTR is an Optical Character Recognition tool powered by deep learning.
Object Detection
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Scaled YOLOv4
Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.
Object Detection
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YOLOR
YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.
Object Detection
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YOLOv5 Oriented Bounding Boxes
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.
Object Detection
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YOLOS
YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.
Object Detection
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4M
The 4M model is a versatile multimodal Transformer model developed by EPFL and Apple, capable of handling a handful of vision and language tasks.
Multimodal Model
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L2CS-Net
L2CS-Net is a gaze estimation model that enables you to calculate where someone is looking and in what direction someone is looking.
Object Detection
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MobileNet SSD v2
This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.
Object Detection
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CoDet
CoDet is an open vocabulary zero-shot object detection model.
Object Detection
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YOLOv4 Darknet
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.
Object Detection
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EfficientDet (D7) Tensorflow 2
A scalable, state of the art object detection model, implemented here within the TensorFlow 2 Object Detection API.
Object Detection
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EfficientDet
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.
Object Detection
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YOLOv4 Tiny
The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset
Object Detection
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RTMDet
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.
Object Detection
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DINOv2
DINOv2 is a self-supervised method for training computer vision models developed by Meta Research and released in April 2023.
Object Detection
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Kosmos-2
Kosmos-2 is a multimodal language model capable of object detection and grounding text in images.
Object Detection
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OWLv2
OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.
Object Detection
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OWL ViT
OWL-ViT is a transformer-based object detection model developed by Google Research.
Object Detection
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GPT-4 with Vision
GPT-4 with Vision is a multimodal language model developed by OpenAI.
Object Detection
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VLPart
VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary
Object Detection
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RT-DETR
Object Detection
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Visual Question Answering
Image Tagging
Image Similarity
Image Captioning
Zero-shot Detection
Real-Time Vision
Image Embedding
LLMS with Vision Capabilities
Multimodal Vision
Foundation Vision