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Add Docs glossary links (#16448)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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10
docs/en/yolov5/tutorials/train_custom_data.md
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@ -77,7 +77,7 @@ Export in `YOLOv5 Pytorch` format, then copy the snippet into your training scri
### 2.1 Create `dataset.yaml`
[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary:
[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of [overfitting](https://www.ultralytics.com/glossary/overfitting). [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary:
```yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
@ -183,11 +183,11 @@ You can use ClearML Data to version your dataset and then pass it to YOLOv5 simp
Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.
This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including [precision](https://www.ultralytics.com/glossary/precision)-[recall](https://www.ultralytics.com/glossary/recall) (PR) curves and confusion matrices.
<img alt="Local logging results" src="https://github.com/ultralytics/docs/releases/download/0/local-logging-results.avif" width="1280">
Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:
Results file `results.csv` is updated after each [epoch](https://www.ultralytics.com/glossary/epoch), and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:
```python
from utils.plots import plot_results
@ -202,8 +202,8 @@ plot_results("path/to/results.csv") # plot 'results.csv' as 'results.png'
Once your model is trained you can use your best checkpoint `best.pt` to:
- Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](./pytorch_hub_model_loading.md) inference on new images and videos
- [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
- [Export](./model_export.md) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
- [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) [accuracy](https://www.ultralytics.com/glossary/accuracy) on train, val and test splits
- [Export](./model_export.md) to [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
- [Evolve](./hyperparameter_evolution.md) hyperparameters to improve performance
- [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset