From ff0b107bf5b19cbcef4ba2926e974e7470626064 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 27 Nov 2024 14:37:25 +0100 Subject: [PATCH] Fix YOLO11 usage (#17834) Signed-off-by: UltralyticsAssistant Co-authored-by: UltralyticsAssistant --- docs/en/integrations/albumentations.md | 4 ++-- examples/YOLO-Series-ONNXRuntime-Rust/README.md | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/en/integrations/albumentations.md b/docs/en/integrations/albumentations.md index fe7b081c2a..3fa4040bac 100644 --- a/docs/en/integrations/albumentations.md +++ b/docs/en/integrations/albumentations.md @@ -52,7 +52,7 @@ Now that we've covered what Albumentations is and what it can do, let's look at ### Installation -To use Albumentations with YOLOv11, start by making sure you have the necessary packages installed. If Albumentations isn't installed, the augmentations won't be applied during training. Once set up, you'll be ready to create an augmented dataset for training, with Albumentations integrated to enhance your model automatically. +To use Albumentations with YOLO11, start by making sure you have the necessary packages installed. If Albumentations isn't installed, the augmentations won't be applied during training. Once set up, you'll be ready to create an augmented dataset for training, with Albumentations integrated to enhance your model automatically. !!! tip "Installation" @@ -67,7 +67,7 @@ For detailed instructions and best practices related to the installation process ### Usage -After installing the necessary packages, you're ready to start using Albumentations with YOLO11. When you train YOLOv11, a set of augmentations is automatically applied through its integration with Albumentations, making it easy to enhance your model's performance. +After installing the necessary packages, you're ready to start using Albumentations with YOLO11. When you train YOLO11, a set of augmentations is automatically applied through its integration with Albumentations, making it easy to enhance your model's performance. !!! example "Usage" diff --git a/examples/YOLO-Series-ONNXRuntime-Rust/README.md b/examples/YOLO-Series-ONNXRuntime-Rust/README.md index ca05fbb180..0b6fabe20d 100644 --- a/examples/YOLO-Series-ONNXRuntime-Rust/README.md +++ b/examples/YOLO-Series-ONNXRuntime-Rust/README.md @@ -62,7 +62,7 @@ cargo run -r -- --task detect --ver v8 --nc 6 --model xxx.onnx # YOLOv8 # Classify cargo run -r -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000 # YOLOv5 cargo run -r -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000 # YOLOv8 -cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLOv11 +cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLO11 # Detect cargo run -r -- --task detect --ver v5 --scale n # YOLOv5 @@ -71,12 +71,12 @@ cargo run -r -- --task detect --ver v7 --scale t # YOLOv7 cargo run -r -- --task detect --ver v8 --scale n # YOLOv8 cargo run -r -- --task detect --ver v9 --scale t # YOLOv9 cargo run -r -- --task detect --ver v10 --scale n # YOLOv10 -cargo run -r -- --task detect --ver v11 --scale n # YOLOv11 +cargo run -r -- --task detect --ver v11 --scale n # YOLO11 cargo run -r -- --task detect --ver rtdetr --scale l # RTDETR # Pose cargo run -r -- --task pose --ver v8 --scale n # YOLOv8-Pose -cargo run -r -- --task pose --ver v11 --scale n # YOLOv11-Pose +cargo run -r -- --task pose --ver v11 --scale n # YOLO11-Pose # Segment cargo run -r -- --task segment --ver v5 --scale n # YOLOv5-Segment @@ -86,7 +86,7 @@ cargo run -r -- --task segment --ver v8 --model yolo/FastSAM-s-dyn-f16.onnx # F # OBB cargo run -r -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv8-Obb -cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv11-Obb +cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLO11-Obb ``` **`cargo run -- --help` for more options**