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Reformat Docs and YAMLs (#12806)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-05-18 22:17:57 +02:00 • committed by GitHub
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5
docs/en/guides/docker-quickstart.md
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@ -93,12 +93,16 @@ sudo docker pull $t
## Running Ultralytics in Docker Container
Here's how to execute the Ultralytics Docker container:
### Using only the CPU
```bash
# Run with all GPUs
sudo docker run -it --ipc=host $t
```
### Using GPUs
```bash
# Run with all GPUs
sudo docker run -it --ipc=host --gpus all $t
@ -109,7 +113,6 @@ sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
The `-it` flag assigns a pseudo-TTY and keeps stdin open, allowing you to interact with the container. The `--ipc=host` flag enables sharing of host's IPC namespace, essential for sharing memory between processes. The `--gpus` flag allows the container to access the host's GPUs.
## Running Ultralytics in Docker Container
Here's how to execute the Ultralytics Docker container:

2
docs/en/guides/nvidia-jetson.md
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@ -23,7 +23,7 @@ NVIDIA Jetson is a series of embedded computing boards designed to bring acceler
[Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/) is the latest iteration of the NVIDIA Jetson family based on NVIDIA Ampere architecture which brings drastically improved AI performance when compared to the previous generations. Below table compared few of the Jetson devices in the ecosystem.
| | Jetson AGX Orin 64GB | Jetson Orin NX 16GB | Jetson Orin Nano 8GB | Jetson AGX Xavier | Jetson Xavier NX | Jetson Nano |
| ----------------- | ---------------------------------------------------------------- | --------------------------------------------------------------- | ------------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------- |
|-------------------|------------------------------------------------------------------|-----------------------------------------------------------------|---------------------------------------------------------------|-------------------------------------------------------------|--------------------------------------------------------------|---------------------------------------------|
| AI Performance | 275 TOPS | 100 TOPS | 40 TOPs | 32 TOPS | 21 TOPS | 472 GFLOPS |
| GPU | 2048-core NVIDIA Ampere architecture GPU with 64 Tensor Cores | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores | 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores | 512-core NVIDIA Volta architecture GPU with 64 Tensor Cores | 384-core NVIDIA Volta™ architecture GPU with 48 Tensor Cores | 128-core NVIDIA Maxwell™ architecture GPU |
| GPU Max Frequency | 1.3 GHz | 918 MHz | 625 MHz | 1377 MHz | 1100 MHz | 921MHz |

2
docs/en/guides/raspberry-pi.md
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@ -130,7 +130,7 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
```
!!! Tip
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options).
## Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks

8
docs/en/guides/vision-eye.md
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@ -166,10 +166,10 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
### `visioneye` Arguments
| Name | Type | Default | Description |
|---------------|---------|------------------|--------------------------------------------------|
| `color` | `tuple` | `(235, 219, 11)` | Line and object centroid color |
| `pin_color` | `tuple` | `(255, 0, 255)` | VisionEye pinpoint color |
| Name | Type | Default | Description |
|-------------|---------|------------------|--------------------------------|
| `color` | `tuple` | `(235, 219, 11)` | Line and object centroid color |
| `pin_color` | `tuple` | `(255, 0, 255)` | VisionEye pinpoint color |
## Note