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ultralytics 8.3.78 new YOLO12 models (#19325)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/models/yolov4.md
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@ -16,7 +16,7 @@ YOLOv4 stands for You Only Look Once version 4. It is a real-time object detecti
## Architecture
YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation), DropBlock [regularization](https://www.ultralytics.com/glossary/regularization), and CIoU loss. These features are combined to achieve state-of-the-art results.
YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-[Batch Normalization](https://www.ultralytics.com/glossary/batch-normalization) (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation), DropBlock [regularization](https://www.ultralytics.com/glossary/regularization), and CIoU loss. These features are combined to achieve state-of-the-art results.
A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet. The neck part of the detector is used to collect feature maps from different stages and usually includes several bottom-up paths and several top-down paths. The head part is what is used to make the final object detections and classifications.
@ -26,7 +26,7 @@ YOLOv4 also makes use of methods known as "bag of freebies," which are technique
## Features and Performance
YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the detection head. This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications. YOLOv4 also excels in accuracy, achieving state-of-the-art results in object detection benchmarks.
YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the [detection head](https://www.ultralytics.com/glossary/detection-head). This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications. YOLOv4 also excels in accuracy, achieving state-of-the-art results in object detection benchmarks.
## Usage Examples
@ -77,7 +77,7 @@ YOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art r
### How does the architecture of YOLOv4 enhance its performance?
The architecture of YOLOv4 includes several key components: the backbone, the neck, and the head. The backbone, which can be models like VGG, ResNet, or CSPDarknet53, is pre-trained to predict classes and bounding boxes. The neck, utilizing PANet, connects feature maps from different stages for comprehensive data extraction. Finally, the head, which uses configurations from YOLOv3, makes the final object detections. YOLOv4 also employs "bag of freebies" techniques like mosaic data augmentation and DropBlock regularization, further optimizing its speed and accuracy.
The architecture of YOLOv4 includes several key components: the [backbone](https://www.ultralytics.com/glossary/backbone), the neck, and the head. The backbone, which can be models like VGG, ResNet, or CSPDarknet53, is pre-trained to predict classes and bounding boxes. The neck, utilizing PANet, connects [feature maps](https://www.ultralytics.com/glossary/feature-maps) from different stages for comprehensive data extraction. Finally, the head, which uses configurations from YOLOv3, makes the final object detections. YOLOv4 also employs "bag of freebies" techniques like mosaic data augmentation and DropBlock regularization, further optimizing its speed and accuracy.
### What are "bag of freebies" in the context of YOLOv4?