HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

[Docs]: Add customization tutorial and address feedback (#155)

Browse source 
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia 2023-01-08 18:31:22 +05:30 • committed by GitHub
parent c985eaba0d
commit d387359f74
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
8 changed files with 133 additions and 74 deletions
Download patch file Download diff file Expand all files Collapse all files

8
docs/config.md
View file

@ -46,7 +46,7 @@ include train, val, and predict.
| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
### Training settings
### Training
Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a
dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings
@ -88,7 +88,7 @@ task.
| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
| dropout | `False` | **Classification**: Use dropout while training |
### Prediction Settings
### Prediction
Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions
with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO
@ -114,7 +114,7 @@ given task.
| agnostic_nms | `False` | Class-agnostic NMS |
| retina_masks | `False` | **Segmentation:** High resolution masks |
### Validation settings
### Validation
Validation settings for YOLO models refer to the various hyperparameters and configurations used to
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
@ -147,7 +147,7 @@ the specific task the model is being used for and the requirements or constraint
It is important to carefully consider and configure these settings to ensure that the exported model is optimized for
the intended use case and can be used effectively in the target environment.
### Augmentation settings
### Augmentation
Augmentation settings for YOLO models refer to the various transformations and modifications
applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's