diff --git a/docs/en/guides/speed-estimation.md b/docs/en/guides/speed-estimation.md index a597f522d0..de3f52701e 100644 --- a/docs/en/guides/speed-estimation.md +++ b/docs/en/guides/speed-estimation.md @@ -10,6 +10,17 @@ keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracki Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](https://docs.ultralytics.com/modes/track/) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes. +

+
+ +
+ Watch: Speed Estimation using Ultralytics YOLOv8 +

+ ## Advantages of Speed Estimation? - **Efficient Traffic Control:** Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways. diff --git a/docs/en/integrations/torchscript.md b/docs/en/integrations/torchscript.md index 68950dbcd8..76a8c9bda1 100644 --- a/docs/en/integrations/torchscript.md +++ b/docs/en/integrations/torchscript.md @@ -12,17 +12,13 @@ Export to Torchscript to serialize your [Ultralytics YOLOv8](https://github.com/ ## Why should you export to TorchScript? -

- TorchScript Overview -

+![Torchscript Overview](https://github.com/ultralytics/ultralytics/assets/26833433/6873349d-c2f6-4620-b3cc-7b26b0698d0b) Developed by the creators of PyTorch, TorchScript is a powerful tool for optimizing and deploying PyTorch models across a variety of platforms. Exporting YOLOv8 models to [TorchScript](https://pytorch.org/docs/stable/jit.html) is crucial for moving from research to real-world applications. TorchScript, part of the PyTorch framework, helps make this transition smoother by allowing PyTorch models to be used in environments that don't support Python. The process involves two techniques: tracing and scripting. Tracing records operations during model execution, while scripting allows for the definition of models using a subset of Python. These techniques ensures that models like YOLOv8 can still work their magic even outside their usual Python environment. -

- TorchScript Script and Trace -

+![TorchScript Script and Trace](https://github.com/ultralytics/ultralytics/assets/26833433/ea9ea24f-a3a9-44bb-aca7-9c358d7490d7) TorchScript models can also be optimized through techniques such as operator fusion and refinements in memory usage, ensuring efficient execution. Another advantage of exporting to TorchScript is its potential to accelerate model execution across various hardware platforms. It creates a standalone, production-ready representation of your PyTorch model that can be integrated into C++ environments, embedded systems, or deployed in web or mobile applications. @@ -30,9 +26,7 @@ TorchScript models can also be optimized through techniques such as operator fus TorchScript, a key part of the PyTorch ecosystem, provides powerful features for optimizing and deploying deep learning models. -

- TorchScript Features -

+![TorchScript Features](https://github.com/ultralytics/ultralytics/assets/26833433/44c7c5e3-1146-42db-952a-9060f070fead) Here are the key features that make TorchScript a valuable tool for developers: diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md index 30e94b8201..d1e9f13617 100644 --- a/docs/en/models/yolov9.md +++ b/docs/en/models/yolov9.md @@ -58,12 +58,12 @@ The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exem **Table 1. Comparison of State-of-the-Art Real-Time Object Detectors** -| Model | Parameters (M) | FLOPs (G) | APval 50:95 (%) | APval 50 (%) | APval 75 (%) | APval S (%) | APval M (%) | APval L (%) | -|----------|----------------|-----------|-----------------|--------------|--------------|-------------|-------------|-------------| -| YOLOv9-S | 7.2 | 26.7 | 46.8 | 63.4 | 50.7 | 26.6 | 56.0 | 64.5 | -| YOLOv9-M | 20.1 | 76.8 | 51.4 | 68.1 | 56.1 | 33.6 | 57.0 | 68.0 | -| YOLOv9-C | 25.5 | 102.8 | 53.0 | 70.2 | 57.8 | 36.2 | 58.5 | 69.3 | -| YOLOv9-E | 58.1 | 192.5 | 55.6 | 72.8 | 60.6 | 40.2 | 61.0 | 71.4 | +| Model | size
(pixels) | APval
50-95 | APval
50 | APval
75 | params
(M) | FLOPs
(B) | +|----------|:---------------------:|:-------------------:|:----------------:|:----------------:|:------------------:|:-----------------:| +| YOLOv9-S | 640 | 46.8 | 63.4 | 50.7 | 7.2 | 26.7 | +| YOLOv9-M | 640 | 51.4 | 68.1 | 56.1 | 20.1 | 76.8 | +| YOLOv9-C | 640 | 53.0 | 70.2 | 57.8 | 25.5 | 102.8 | +| YOLOv9-E | 640 | 55.6 | 72.8 | 60.6 | 58.1 | 192.5 | YOLOv9's iterations, ranging from the smaller S variant to the extensive E model, demonstrate improvements not only in accuracy (AP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high precision while maintaining or reducing the computational overhead compared to prior versions and competing models. @@ -72,7 +72,7 @@ Comparatively, YOLOv9 exhibits remarkable gains: - **Lightweight Models**: YOLOv9-S surpasses the YOLO MS-S in parameter efficiency and computational load while achieving an improvement of 0.4∼0.6% in AP. - **Medium to Large Models**: YOLOv9-M and YOLOv9-E show notable advancements in balancing the trade-off between model complexity and detection performance, offering significant reductions in parameters and computations against the backdrop of improved accuracy. -The YOLOv9-C model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9-E model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than YOLOv8-X, alongside a substantial 1.7% improvement in AP. +The YOLOv9-C model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9-E model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside a substantial 1.7% improvement in AP. These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of computer vision. diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py index afe7d3e6c2..ed5cec57cb 100644 --- a/ultralytics/engine/exporter.py +++ b/ultralytics/engine/exporter.py @@ -344,8 +344,8 @@ class Exporter: requirements = ["onnx>=1.12.0"] if self.args.simplify: requirements += ["onnxsim>=0.4.33", "onnxruntime-gpu" if torch.cuda.is_available() else "onnxruntime"] - if ARM64: - check_requirements("cmake") # 'cmake' is needed to build onnxsim on aarch64 + if ARM64: + check_requirements("cmake") # 'cmake' is needed to build onnxsim on aarch64 check_requirements(requirements) import onnx # noqa