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PyCharm Code and Docs Inspect fixes v1 (#18461)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2025-01-03 01:16:18 +05:00 • committed by GitHub
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docs/en/yolov5/tutorials/clearml_logging_integration.md
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@ -102,7 +102,7 @@ Versioning your data separately from your code is generally a good idea and make
### Prepare Your Dataset
The YOLOv5 repository supports a number of different datasets by using YAML files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the YAML or with the scripts provided by yolov5, you get this folder structure:
The YOLOv5 repository supports a number of different datasets by using YAML files containing their information. By default, datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the YAML or with the scripts provided by yolov5, you get this folder structure:
```
..

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docs/en/yolov5/tutorials/comet_logging_integration.md
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@ -138,7 +138,7 @@ python train.py \
### Controlling the number of Prediction Images logged to Comet
When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable.
When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default, a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable.
```shell
env COMET_MAX_IMAGE_UPLOADS=200 python train.py \

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docs/en/yolov5/tutorials/tips_for_best_training_results.md
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@ -18,7 +18,7 @@ We've put together a full guide for users looking to get the best results on the
- **Instances per class.** ≥ 10000 instances (labeled objects) per class recommended
- **Image variety.** Must be representative of deployed environment. For real-world use cases we recommend images from different times of day, different seasons, different weather, different lighting, different angles, different sources (scraped online, collected locally, different cameras) etc.
- **Label consistency.** All instances of all classes in all images must be labelled. Partial labelling will not work.
- **Label [accuracy](https://www.ultralytics.com/glossary/accuracy).** Labels must closely enclose each object. No space should exist between an object and it's [bounding box](https://www.ultralytics.com/glossary/bounding-box). No objects should be missing a label.
- **Label [accuracy](https://www.ultralytics.com/glossary/accuracy).** Labels must closely enclose each object. No space should exist between an object, and it's [bounding box](https://www.ultralytics.com/glossary/bounding-box). No objects should be missing a label.
- **Label verification.** View `train_batch*.jpg` on train start to verify your labels appear correct, i.e. see [example](./train_custom_data.md#local-logging) mosaic.
- **Background images.** Background images are images with no objects that are added to a dataset to reduce False Positives (FP). We recommend about 0-10% background images to help reduce FPs (COCO has 1000 background images for reference, 1% of the total). No labels are required for background images.