Compare YOLOv4 vs YOLOv7 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
YOLOv4 vs YOLOv7 Comparison Table
| Property | YOLOv4 | YOLOv7 |
|---|---|---|
| Organization | Academia Sinica | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Apr 2020 | Jul 2022 |
| Context Window | — | — |
| Parameters | Unknown | 6.2M-151.7M |
| License | Custom | GPL v3 |
| Commercial use | See license terms | commercial use means releasing your source code under GPL-3.0 |
| Vision Tasks | ||
Both support 1 more task | ||
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLOv4 vs YOLOv7: Overview
YOLOv4 is an object detection model developed by Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao at Academia Sinica, released in April 2020 via the Darknet framework. It combines a CSPDarknet53 backbone, PANet neck, and YOLOv3 detection head with a large set of training improvements — Bag of Freebies and Bag of Specials — that improve accuracy with minimal inference cost increase.
YOLOv4 achieves 43.5% AP on COCO at 65 FPS on a Tesla V100 GPU. The Darknet implementation is the original version, distinguishing it from subsequent PyTorch-based reimplementations. It remains a widely referenced detection architecture and a supported training target in Roboflow Inference.
YOLOv7 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in July 2022 under the GPL-3.0 license. It introduces Extended Efficient Layer Aggregation Networks (E-ELAN) for improved gradient flow in the backbone, and trainable bag-of-freebies techniques including coarse-to-fine lead guided label assignment and auxiliary heads that improve accuracy without adding inference cost.
YOLOv7 achieves 56.8% AP on COCO at 30 FPS on a V100 GPU at the time of release, establishing a strong accuracy-speed tradeoff among real-time detectors. It supports detection, instance segmentation, and pose estimation variants. YOLOv7 is deployable through Roboflow Inference and the standard training pipeline in the official repository.
