Capitalize OBB acronym (#7383)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

README.md

@@ -187,9 +187,9 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
 
 </details>
 
-<details><summary>Obb (DOTAv1)</summary>
+<details><summary>OBB (DOTAv1)</summary>
 
-See [Obb Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
+See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
 
 | Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |

docs/en/datasets/explorer/api.md

@@ -38,11 +38,10 @@ dataframe = explorer.get_similar()(idx=0)
 
 ## 1. Similarity Search
 
-Similarity search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. One the embeddings table is built, you can get run semantic search in any of the following ways:
+Similarity search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. Once the embeddings table is built, you can get run semantic search in any of the following ways:
 
-- On a given index / list of indices in the dataset like - `exp.get_similar(idx=[1,10], limit=10)`
-- On any image/ list of images not in the dataset - `exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10)`
--
+- On a given index or list of indices in the dataset: `exp.get_similar(idx=[1,10], limit=10)`
+- On any image or list of images not in the dataset: `exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10)`
 
 In case of multiple inputs, the aggregate of their embeddings is used.
 

docs/en/models/yolov7.md

@@ -8,8 +8,8 @@ keywords: YOLOv7, real-time object detector, state-of-the-art, Ultralytics, MS C
 
 YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
 
-![YOLOv7 comparison with SOTA object detectors](https://github.com/ultralytics/ultralytics/assets/26833433/5e1e0420-8122-4c79-b8d0-2860aa79af92) **Comparison of state-of-the-art object detectors.
-** From the results in Table 2 we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L. If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 fps inference speed, YOLOv7-X can improve AP by 3.9%. If YOLOv7-X is compared with YOLOv5-X (r6.1) of similar scale, the inference speed of YOLOv7-X is 31 fps faster. In addition, in terms the amount of parameters and computation, YOLOv7-X reduces 22% of parameters and 8% of computation compared to YOLOv5-X (r6.1), but improves AP by 2.2% ([Source](https://arxiv.org/pdf/2207.02696.pdf)).
+![YOLOv7 comparison with SOTA object detectors](https://github.com/ultralytics/ultralytics/assets/26833433/5e1e0420-8122-4c79-b8d0-2860aa79af92)
+**Comparison of state-of-the-art object detectors.** From the results in Table 2 we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L. If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 fps inference speed, YOLOv7-X can improve AP by 3.9%. If YOLOv7-X is compared with YOLOv5-X (r6.1) of similar scale, the inference speed of YOLOv7-X is 31 fps faster. In addition, in terms the amount of parameters and computation, YOLOv7-X reduces 22% of parameters and 8% of computation compared to YOLOv5-X (r6.1), but improves AP by 2.2% ([Source](https://arxiv.org/pdf/2207.02696.pdf)).
 
 ## Overview
 

docs/en/models/yolov8.md

@@ -127,7 +127,7 @@ This table provides an overview of the YOLOv8 model variants, highlighting their
 
 This example provides simple YOLOv8 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
 
-Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [Obb](../tasks/obb.md) docs and [Pose](../tasks/pose.md) docs.
+Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md) docs and [Pose](../tasks/pose.md) docs.
 
 !!! Example
 

docs/en/modes/track.md

@@ -354,10 +354,5 @@ To initiate your contribution, please refer to our [Contributing Guide](https://
 Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
 
 [fish track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142
-
 [people track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
 [vehicle track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab
-
-[people track]:  https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
-
-[fish track]:    https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142

docs/en/tasks/index.md

@@ -46,7 +46,7 @@ Pose/keypoint detection is a task that involves detecting specific points in an
 
 [Pose Examples](pose.md){ .md-button }
 
-## [Obb](obb.md)
+## [OBB](obb.md)
 
 Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLOv8 can detect rotated objects in an image or video frame with high accuracy and speed.
 

docs/en/tasks/obb.md

@@ -16,11 +16,11 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
 
 !!! Tip "Tip"
 
-    YOLOv8 Obb models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
+    YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
 
 ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
 
-YOLOv8 pretrained Obb models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset.
+YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset.
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 

docs/en/yolov5/tutorials/running_on_jetson_nano.md

@@ -84,15 +84,15 @@ Here are some of the versions supported by JetPack 4.6 and above.
 
 Supported by JetPack 4.4 (L4T R32.4.3) / JetPack 4.4.1 (L4T R32.4.4) / JetPack 4.5 (L4T R32.5.0) / JetPack 4.5.1 (L4T R32.5.1) / JetPack 4.6 (L4T R32.6.1) with Python 3.6
 
-* **file_name:** torch-1.10.0-cp36-cp36m-linux_aarch64.whl
-* **URL:** [https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl](https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl)
+- **file_name:** torch-1.10.0-cp36-cp36m-linux_aarch64.whl
+- **URL:** [https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl](https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl)
 
 **PyTorch v1.12.0**
 
 Supported by JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T R34.1.1) / JetPack 5.0.2 (L4T R35.1.0) with Python 3.8
 
-* **file_name:** torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl
-* **URL:** [https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl](https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl)
+- **file_name:** torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl
+- **URL:** [https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl](https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl)
 
 - **Step 1.** Install torch according to your JetPack version in the following format
 

docs/mkdocs.yml

@@ -169,7 +169,7 @@ nav:
           - Segment: tasks/segment.md
           - Classify: tasks/classify.md
           - Pose: tasks/pose.md
-          - Obb: tasks/obb.md
+          - OBB: tasks/obb.md
       - Guides:
           - guides/index.md
       - Models:
@@ -203,7 +203,7 @@ nav:
       - Segment: tasks/segment.md
       - Classify: tasks/classify.md
       - Pose: tasks/pose.md
-      - Obb: tasks/obb.md
+      - OBB: tasks/obb.md
   - Models:
       - models/index.md
       - YOLOv3: models/yolov3.md