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docs/cfg.md
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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
and hyperparameters can affect the model's behavior at various stages of the model development process, including
training, validation, and prediction.
Properly setting and tuning these parameters can have a significant impact on the model's ability to learn effectively
from the training data and generalize to new data. For example, choosing an appropriate learning rate, batch size, and
optimization algorithm can greatly affect the model's convergence speed and accuracy. Similarly, setting the correct
confidence threshold and non-maximum suppression (NMS) threshold can affect the model's performance on detection tasks.
It is important to carefully consider and experiment with these settings and hyperparameters to achieve the best
possible performance for a given task. This can involve trial and error, as well as using techniques such as
hyperparameter optimization to search for the optimal set of parameters.
In summary, YOLO settings and hyperparameters are a key factor in the success of a YOLO model, and it is important to
pay careful attention to them to achieve the desired results.
### Setting the operation type
YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks
differ in the type of output they produce and the specific problem they are designed to solve.
- Detection: Detection tasks involve identifying and localizing objects or regions of interest in an image or video.
YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an
image.
- Segmentation: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to
different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for
each pixel in an image.
- Classification: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO
models can be used for image classification tasks by predicting the class label of an input image.
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
include train, val, and predict.
- Train: The train mode is used to train the model on a dataset. This mode is typically used during the development and
testing phase of a model.
- Val: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used to
tune the model's hyperparameters and detect overfitting.
- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
production or when deploying the model to users.
| Key | Value | Description |
|--------|----------|-----------------------------------------------------------------------------------------------|
| task | 'detect' | inference task, i.e. detect, segment, or classify |
| mode | 'train' | YOLO mode, i.e. train, val, predict, or export |
| resume | False | resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt |
| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| data | null | path to data file, i.e. i.e. coco128.yaml |
### 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
include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
task.
| Key | Value | Description |
|-----------------|--------|-----------------------------------------------------------------------------|
| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| data | null | path to data file, i.e. i.e. coco128.yaml |
| epochs | 100 | number of epochs to train for |
| patience | 50 | epochs to wait for no observable improvement for early stopping of training |
| batch | 16 | number of images per batch (-1 for AutoBatch) |
| imgsz | 640 | size of input images as integer or w,h |
| save | True | save train checkpoints and predict results |
| cache | False | True/ram, disk or False. Use cache for data loading |
| device | null | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
| workers | 8 | number of worker threads for data loading (per RANK if DDP) |
| project | null | project name |
| name | null | experiment name |
| exist_ok | False | whether to overwrite existing experiment |
| pretrained | False | whether to use a pretrained model |
| optimizer | 'SGD' | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp'] |
| verbose | False | whether to print verbose output |
| seed | 0 | random seed for reproducibility |
| deterministic | True | whether to enable deterministic mode |
| single_cls | False | train multi-class data as single-class |
| image_weights | False | use weighted image selection for training |
| rect | False | support rectangular training |
| cos_lr | False | use cosine learning rate scheduler |
| close_mosaic | 10 | disable mosaic augmentation for final 10 epochs |
| resume | False | resume training from last checkpoint |
| lr0 | 0.01 | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| lrf | 0.01 | final learning rate (lr0 * lrf) |
| momentum | 0.937 | SGD momentum/Adam beta1 |
| weight_decay | 0.0005 | optimizer weight decay 5e-4 |
| warmup_epochs | 3.0 | warmup epochs (fractions ok) |
| warmup_momentum | 0.8 | warmup initial momentum |
| warmup_bias_lr | 0.1 | warmup initial bias lr |
| box | 7.5 | box loss gain |
| cls | 0.5 | cls loss gain (scale with pixels) |
| dfl | 1.5 | dfl loss gain |
| fl_gamma | 0.0 | focal loss gamma (efficientDet default gamma=1.5) |
| label_smoothing | 0.0 | label smoothing (fraction) |
| nbs | 64 | nominal batch size |
| overlap_mask | True | masks should overlap during training (segment train only) |
| mask_ratio | 4 | mask downsample ratio (segment train only) |
| dropout | 0.0 | use dropout regularization (classify train only) |
### 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
prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes
to consider. Other factors that may affect the prediction process include the size and format of the input data, the
presence of additional features such as masks or multiple labels per box, and the specific task the model is being used
for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a
given task.
| Key | Value | Description |
|----------------|----------------------|---------------------------------------------------------|
| source | 'ultralytics/assets' | source directory for images or videos |
| show | False | show results if possible |
| save_txt | False | save results as .txt file |
| save_conf | False | save results with confidence scores |
| save_crop | Fasle | save cropped images with results |
| hide_labels | False | hide labels |
| hide_conf | False | hide confidence scores |
| vid_stride | False | video frame-rate stride |
| line_thickness | 3 | bounding box thickness (pixels) |
| visualize | False | visualize model features |
| augment | False | apply image augmentation to prediction sources |
| agnostic_nms | False | class-agnostic NMS |
| retina_masks | False | use high-resolution segmentation masks |
| classes | null | filter results by class, i.e. class=0, or class=[0,2,3] |
### 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
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
process include the size and composition of the validation dataset and the specific task the model is being used for. It
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
validation dataset and to detect and prevent overfitting.
| Key | Value | Description |
|-------------|-------|-----------------------------------------------------------------------------|
| val | True | validate/test during training |
| save_json | False | save results to JSON file |
| save_hybrid | False | save hybrid version of labels (labels + additional predictions) |
| conf | 0.001 | object confidence threshold for detection (default 0.25 predict, 0.001 val) |
| iou | 0.6 | intersection over union (IoU) threshold for NMS |
| max_det | 300 | maximum number of detections per image |
| half | True | use half precision (FP16) |
| dnn | False | use OpenCV DNN for ONNX inference |
| plots | False | show plots during training |
### Export
Export settings for YOLO models refer to the various configurations and options used to save or
export the model for use in other environments or platforms. These settings can affect the model's performance, size,
and compatibility with different systems. Some common YOLO export settings include the format of the exported model
file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
additional features such as masks or multiple labels per box. Other factors that may affect the export process include
the specific task the model is being used for and the requirements or constraints of the target environment or platform.
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
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
performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the
transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each
transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other
factors that may affect the augmentation process include the size and composition of the original dataset and the
specific task the model is being used for. It is important to carefully tune and experiment with these settings to
ensure that the augmented dataset is diverse and representative enough to train a high-performing model.
| Key | Value | Description |
|-------------|-------|-------------------------------------------------|
| hsv_h | 0.015 | image HSV-Hue augmentation (fraction) |
| hsv_s | 0.7 | image HSV-Saturation augmentation (fraction) |
| hsv_v | 0.4 | image HSV-Value augmentation (fraction) |
| degrees | 0.0 | image rotation (+/- deg) |
| translate | 0.1 | image translation (+/- fraction) |
| scale | 0.5 | image scale (+/- gain) |
| shear | 0.0 | image shear (+/- deg) |
| perspective | 0.0 | image perspective (+/- fraction), range 0-0.001 |
| flipud | 0.0 | image flip up-down (probability) |
| fliplr | 0.5 | image flip left-right (probability) |
| mosaic | 1.0 | image mosaic (probability) |
| mixup | 0.0 | image mixup (probability) |
| copy_paste | 0.0 | segment copy-paste (probability) |
### Logging, checkpoints, plotting and file management
Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.
- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and
diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log
messages to a file.
- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows
you to resume training from a previous point if the training process is interrupted or if you want to experiment with
different training configurations.
- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is
behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by
generating plots using a logging library such as TensorBoard.
- File management: Managing the various files generated during the training process, such as model checkpoints, log
files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of
these files and make it easy to access and analyze them as needed.
Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make
it easier to debug and optimize the training process.
| Key | Value | Description |
|----------|--------|------------------------------------------------------------------------------------------------|
| project | 'runs' | project name |
| name | 'exp' | experiment name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| exist_ok | False | whether to overwrite existing experiment |
| plots | False | save plots during train/val |
| save | False | save train checkpoints and predict results |

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docs/cli.md
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@ -17,7 +17,7 @@ Where:
the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export]`
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
For a full list of available `ARGS` see the [Configuration](config.md) page.
For a full list of available `ARGS` see the [Configuration](cfg.md) page.
!!! note ""
@ -30,7 +30,7 @@ Where:
## Train
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
the [Configuration](config.md) page.
the [Configuration](cfg.md) page.
!!! example ""

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docs/config.md
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@ -1,202 +0,0 @@
YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
and hyperparameters can affect the model's behavior at various stages of the model development process, including
training, validation, and prediction.
Properly setting and tuning these parameters can have a significant impact on the model's ability to learn effectively
from the training data and generalize to new data. For example, choosing an appropriate learning rate, batch size, and
optimization algorithm can greatly affect the model's convergence speed and accuracy. Similarly, setting the correct
confidence threshold and non-maximum suppression (NMS) threshold can affect the model's performance on detection tasks.
It is important to carefully consider and experiment with these settings and hyperparameters to achieve the best
possible performance for a given task. This can involve trial and error, as well as using techniques such as
hyperparameter optimization to search for the optimal set of parameters.
In summary, YOLO settings and hyperparameters are a key factor in the success of a YOLO model, and it is important to
pay careful attention to them to achieve the desired results.
### Setting the operation type
YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks
differ in the type of output they produce and the specific problem they are designed to solve.
- Detection: Detection tasks involve identifying and localizing objects or regions of interest in an image or video.
YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an
image.
- Segmentation: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to
different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for
each pixel in an image.
- Classification: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO
models can be used for image classification tasks by predicting the class label of an input image.
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
include train, val, and predict.
- Train: The train mode is used to train the model on a dataset. This mode is typically used during the development and
testing phase of a model.
- Val: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used to
tune the model's hyperparameters and detect overfitting.
- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
production or when deploying the model to users.
| Key | Value | Description |
|--------|----------|--------------------------------------------------------------------------------------------------------|
| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify` |
| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict`, `export` |
| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
| 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
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
include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
task.
| Key | Value | Description |
|-----------------|---------|-----------------------------------------------------------------------------|
| device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
| epochs | 100 | Number of epochs to train |
| workers | 8 | Number of cpu workers used per process. Scales automatically with DDP |
| batch | 16 | Batch size of the dataloader |
| imgsz | 640 | Image size of data in dataloader |
| optimizer | SGD | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp` |
| single_cls | False | Train on multi-class data as single-class |
| image_weights | False | Use weighted image selection for training |
| rect | False | Enable rectangular training |
| cos_lr | False | Use cosine LR scheduler |
| lr0 | 0.01 | Initial learning rate |
| lrf | 0.01 | Final OneCycleLR learning rate |
| momentum | 0.937 | Use as `momentum` for SGD and `beta1` for Adam |
| weight_decay | 0.0005 | Optimizer weight decay |
| warmup_epochs | 3.0 | Warmup epochs. Fractions are ok. |
| warmup_momentum | 0.8 | Warmup initial momentum |
| warmup_bias_lr | 0.1 | Warmup initial bias lr |
| box | 0.05 | Box loss gain |
| cls | 0.5 | cls loss gain |
| cls_pw | 1.0 | cls BCELoss positive_weight |
| obj | 1.0 | bj loss gain (scale with pixels) |
| obj_pw | 1.0 | obj BCELoss positive_weight |
| iou_t | 0.20 | IOU training threshold |
| anchor_t | 4.0 | anchor-multiple threshold |
| fl_gamma | 0.0 | focal loss gamma |
| label_smoothing | 0.0 | |
| nbs | 64 | nominal batch size |
| overlap_mask | `True` | **Segmentation**: Use mask overlapping during training |
| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
| dropout | `False` | **Classification**: Use dropout while training |
### 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
prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes
to consider. Other factors that may affect the prediction process include the size and format of the input data, the
presence of additional features such as masks or multiple labels per box, and the specific task the model is being used
for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a
given task.
| Key | Value | Description |
|----------------|----------------------|-------------------------------------------------|
| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
| show | `False` | View the prediction images |
| save_txt | `False` | Save the results in a txt file |
| save_conf | `False` | Save the condidence scores |
| save_crop | `Fasle` | |
| hide_labels | `False` | Hide the labels |
| hide_conf | `False` | Hide the confidence scores |
| vid_stride | `False` | Input video frame-rate stride |
| line_thickness | `3` | Bounding-box thickness (pixels) |
| visualize | `False` | Visualize model features |
| augment | `False` | Augmented inference |
| agnostic_nms | `False` | Class-agnostic NMS |
| retina_masks | `False` | **Segmentation:** High resolution masks |
### 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
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
process include the size and composition of the validation dataset and the specific task the model is being used for. It
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
validation dataset and to detect and prevent overfitting.
| Key | Value | Description |
|-------------|---------|-----------------------------------|
| noval | `False` | ??? |
| save_json | `False` | |
| save_hybrid | `False` | |
| conf | `0.001` | Confidence threshold |
| iou | `0.6` | IoU threshold |
| max_det | `300` | Maximum number of detections |
| half | `True` | Use .half() mode. |
| dnn | `False` | Use OpenCV DNN for ONNX inference |
| plots | `False` | |
### Export
Export settings for YOLO models refer to the various configurations and options used to save or
export the model for use in other environments or platforms. These settings can affect the model's performance, size,
and compatibility with different systems. Some common YOLO export settings include the format of the exported model
file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
additional features such as masks or multiple labels per box. Other factors that may affect the export process include
the specific task the model is being used for and the requirements or constraints of the target environment or platform.
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
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
performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the
transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each
transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other
factors that may affect the augmentation process include the size and composition of the original dataset and the
specific task the model is being used for. It is important to carefully tune and experiment with these settings to
ensure that the augmented dataset is diverse and representative enough to train a high-performing model.
| hsv_h | 0.015 | Image HSV-Hue augmentation (fraction) |
|-------------|-------|-------------------------------------------------|
| hsv_s | 0.7 | Image HSV-Saturation augmentation (fraction) |
| hsv_v | 0.4 | Image HSV-Value augmentation (fraction) |
| degrees | 0.0 | Image rotation (+/- deg) |
| translate | 0.1 | Image translation (+/- fraction) |
| scale | 0.5 | Image scale (+/- gain) |
| shear | 0.0 | Image shear (+/- deg) |
| perspective | 0.0 | Image perspective (+/- fraction), range 0-0.001 |
| flipud | 0.0 | Image flip up-down (probability) |
| fliplr | 0.5 | Image flip left-right (probability) |
| mosaic | 1.0 | Image mosaic (probability) |
| mixup | 0.0 | Image mixup (probability) |
| copy_paste | 0.0 | Segment copy-paste (probability) |
### Logging, checkpoints, plotting and file management
Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.
- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and
diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log
messages to a file.
- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows
you to resume training from a previous point if the training process is interrupted or if you want to experiment with
different training configurations.
- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is
behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by
generating plots using a logging library such as TensorBoard.
- File management: Managing the various files generated during the training process, such as model checkpoints, log
files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of
these files and make it easy to access and analyze them as needed.
Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make
it easier to debug and optimize the training process.
| Key | Value | Description |
|-----------|---------|---------------------------------------------------------------------------------------------|
| project: | 'runs' | The project name |
| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| exist_ok: | `False` | Will replace current directory contents if set to True and output directory exists. |
| plots | `False` | **Validation**: Save plots while validation |
| save | `False` | Save any plots, models or files |

18
docs/predict.md
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@ -48,19 +48,21 @@ box.xyxy
```
- Properties and conversions
```
results.boxes.xyxy # box with xyxy format, (N, 4)
results.boxes.xywh # box with xywh format, (N, 4)
results.boxes.xyxyn # box with xyxy format but normalized, (N, 4)
results.boxes.xywhn # box with xywh format but normalized, (N, 4)
results.boxes.conf # confidence score, (N, 1)
results.boxes.cls # cls, (N, 1)
boxes.xyxy # box with xyxy format, (N, 4)
boxes.xywh # box with xywh format, (N, 4)
boxes.xyxyn # box with xyxy format but normalized, (N, 4)
boxes.xywhn # box with xywh format but normalized, (N, 4)
boxes.conf # confidence score, (N, 1)
boxes.cls # cls, (N, 1)
boxes.data # raw bboxes tensor, (N, 6) or boxes.boxes .
```
### Masks
`Masks` object can be used index, manipulate and convert masks to segments. The segment conversion operation is cached.
```python
results.masks.masks # masks, (N, H, W)
results.masks.segments # bounding coordinates of masks, List[segment] * N
masks = results.masks # Masks object
masks.segments # bounding coordinates of masks, List[segment] * N
masks.data # raw masks tensor, (N, H, W) or masks.masks
```
### probs

2
docs/tasks/classification.md
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@ -16,7 +16,7 @@ of that class are located or what their exact shape is.
## Train
Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments
see the [Configuration](../config.md) page.
see the [Configuration](../cfg.md) page.
!!! example ""

2
docs/tasks/detection.md
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@ -16,7 +16,7 @@ scene, but don't need to know exactly where the object is or its exact shape.
## Train
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
the [Configuration](../config.md) page.
the [Configuration](../cfg.md) page.
!!! example ""

2
docs/tasks/segmentation.md
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@ -16,7 +16,7 @@ segmentation is useful when you need to know not only where objects are in an im
## Train
Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available
arguments see the [Configuration](../config.md) page.
arguments see the [Configuration](../cfg.md) page.
!!! example ""

2
mkdocs.yml
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@ -85,7 +85,7 @@ nav:
- CLI: cli.md
- Python: python.md
- Predict: predict.md
- Configuration: config.md
- Configuration: cfg.md
- Customization Guide: engine.md
- Ultralytics HUB: hub.md
- iOS and Android App: app.md

3
setup.py
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@ -51,5 +51,4 @@ setup(
"Operating System :: MacOS", "Operating System :: Microsoft :: Windows"],
keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics",
entry_points={
'console_scripts':
['yolo = ultralytics.yolo.configs:entrypoint', 'ultralytics = ultralytics.yolo.configs:entrypoint']})
'console_scripts': ['yolo = ultralytics.yolo.cfg:entrypoint', 'ultralytics = ultralytics.yolo.cfg:entrypoint']})

4
tests/test_engine.py
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@ -2,14 +2,14 @@
from pathlib import Path
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, ROOT, SETTINGS
from ultralytics.yolo.v8 import classify, detect, segment
CFG_DET = 'yolov8n.yaml'
CFG_SEG = 'yolov8n-seg.yaml'
CFG_CLS = 'squeezenet1_0'
CFG = get_config(DEFAULT_CFG_PATH)
CFG = get_cfg(DEFAULT_CFG_PATH)
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
SOURCE = ROOT / "assets"

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = "8.0.12"
__version__ = "8.0.14"
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils import ops

4
ultralytics/hub/utils.py
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@ -136,12 +136,12 @@ def sync_analytics(cfg, all_keys=False, enabled=False):
Sync analytics data if enabled in the global settings
Args:
cfg (DictConfig): Configuration for the task and mode.
cfg (UltralyticsCFG): Configuration for the task and mode.
all_keys (bool): Sync all items, not just non-default values.
enabled (bool): For debugging.
"""
if SETTINGS['sync'] and RANK in {-1, 0} and enabled:
cfg = dict(cfg) # convert type from DictConfig to dict
cfg = dict(cfg) # convert type from UltralyticsCFG to dict
if not all_keys:
cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None)} # retain non-default values
cfg['uuid'] = SETTINGS['uuid'] # add the device UUID to the configuration data

1
ultralytics/nn/tasks.py
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@ -95,7 +95,6 @@ class BaseModel(nn.Module):
(nn.Module): The fused model is returned.
"""
if not self.is_fused():
LOGGER.info('Fusing... ')
for m in self.model.modules():
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv

37
ultralytics/yolo/configs/__init__.py → ultralytics/yolo/cfg/__init__.py
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@ -28,7 +28,7 @@ CLI_HELP_MSG = \
Where TASK (optional) is one of [detect, segment, classify]
MODE (required) is one of [train, val, predict, export]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
For a full list of available ARGS see https://docs.ultralytics.com/config.
For a full list of available ARGS see https://docs.ultralytics.com/cfg.
Train a detection model for 10 epochs with an initial learning_rate of 0.01
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
@ -48,7 +48,7 @@ CLI_HELP_MSG = \
yolo checks
yolo version
yolo settings
yolo copy-config
yolo copy-cfg
Docs: https://docs.ultralytics.com/cli
Community: https://community.ultralytics.com
@ -56,6 +56,15 @@ CLI_HELP_MSG = \
"""
class UltralyticsCFG(SimpleNamespace):
"""
UltralyticsCFG iterable SimpleNamespace class to allow SimpleNamespace to be used with dict() and in for loops
"""
def __iter__(self):
return iter(vars(self).items())
def cfg2dict(cfg):
"""
Convert a configuration object to a dictionary.
@ -75,30 +84,30 @@ def cfg2dict(cfg):
return cfg
def get_config(config: Union[str, Path, Dict, SimpleNamespace], overrides: Dict = None):
def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace], overrides: Dict = None):
"""
Load and merge configuration data from a file or dictionary.
Args:
config (str) or (Path) or (Dict) or (SimpleNamespace): Configuration data.
cfg (str) or (Path) or (Dict) or (SimpleNamespace): Configuration data.
overrides (str) or (Dict), optional: Overrides in the form of a file name or a dictionary. Default is None.
Returns:
(SimpleNamespace): Training arguments namespace.
"""
config = cfg2dict(config)
cfg = cfg2dict(cfg)
# Merge overrides
if overrides:
overrides = cfg2dict(overrides)
check_config_mismatch(config, overrides)
config = {**config, **overrides} # merge config and overrides dicts (prefer overrides)
check_cfg_mismatch(cfg, overrides)
cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides)
# Return instance
return SimpleNamespace(**config)
return UltralyticsCFG(**cfg)
def check_config_mismatch(base: Dict, custom: Dict):
def check_cfg_mismatch(base: Dict, custom: Dict):
"""
This function checks for any mismatched keys between a custom configuration list and a base configuration list.
If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
@ -127,8 +136,8 @@ def entrypoint(debug=False):
- running special modes like 'checks'
- passing overrides to the package's configuration
It uses the package's default config and initializes it using the passed overrides.
Then it calls the CLI function with the composed config
It uses the package's default cfg and initializes it using the passed overrides.
Then it calls the CLI function with the composed cfg
"""
if debug:
args = ['train', 'predict', 'model=yolov8n.pt'] # for testing
@ -149,7 +158,7 @@ def entrypoint(debug=False):
'checks': checks.check_yolo,
'version': lambda: LOGGER.info(__version__),
'settings': print_settings,
'copy-config': copy_default_config}
'copy-cfg': copy_default_config}
overrides = {} # basic overrides, i.e. imgsz=320
defaults = yaml_load(DEFAULT_CFG_PATH)
@ -190,7 +199,7 @@ def entrypoint(debug=False):
f"https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/configs/default.yaml"
f"\n{CLI_HELP_MSG}")
cfg = get_config(defaults, overrides) # create CFG instance
cfg = get_cfg(defaults, overrides) # create CFG instance
# Mapping from task to module
module = {"detect": yolo.v8.detect, "segment": yolo.v8.segment, "classify": yolo.v8.classify}.get(cfg.task)
@ -214,7 +223,7 @@ def copy_default_config():
new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
shutil.copy2(DEFAULT_CFG_PATH, new_file)
LOGGER.info(f"{PREFIX}{DEFAULT_CFG_PATH} copied to {new_file}\n"
f"Usage for running YOLO with this new custom config:\nyolo cfg={new_file} args...")
f"Usage for running YOLO with this new custom cfg:\nyolo cfg={new_file} args...")
if __name__ == '__main__':

39
ultralytics/yolo/configs/default.yaml → ultralytics/yolo/cfg/default.yaml
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@ -1,20 +1,20 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
task: "detect" # inference task, i.e. detect, segment, classify
mode: "train" # YOLO mode, i.e. train, val, predict, export
# Train settings -------------------------------------------------------------------------------------------------------
model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
data: null # i.e. coco128.yaml. Path to data file
model: null # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: null # path to data file, i.e. i.e. coco128.yaml
epochs: 100 # number of epochs to train for
patience: 50 # epochs to wait for no observable improvement for early stopping of training
batch: 16 # number of images per batch
imgsz: 640 # size of input images
save: True # save checkpoints
batch: 16 # number of images per batch (-1 for AutoBatch)
imgsz: 640 # size of input images as integer or w,h
save: True # save train checkpoints and predict results
cache: False # True/ram, disk or False. Use cache for data loading
device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
workers: 8 # number of worker threads for data loading
device: null # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8 # number of worker threads for data loading (per RANK if DDP)
project: null # project name
name: null # experiment name
exist_ok: False # whether to overwrite existing experiment
@ -30,10 +30,10 @@ cos_lr: False # use cosine learning rate scheduler
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
resume: False # resume training from last checkpoint
# Segmentation
overlap_mask: True # masks should overlap during training
mask_ratio: 4 # mask downsample ratio
overlap_mask: True # masks should overlap during training (segment train only)
mask_ratio: 4 # mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # use dropout regularization
dropout: 0.0 # use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # validate/test during training
@ -44,7 +44,7 @@ iou: 0.7 # intersection over union (IoU) threshold for NMS
max_det: 300 # maximum number of detections per image
half: False # use half precision (FP16)
dnn: False # use OpenCV DNN for ONNX inference
plots: True # show plots during training
plots: True # save plots during train/val
# Prediction settings --------------------------------------------------------------------------------------------------
source: null # source directory for images or videos
@ -56,10 +56,11 @@ hide_labels: False # hide labels
hide_conf: False # hide confidence scores
vid_stride: 1 # video frame-rate stride
line_thickness: 3 # bounding box thickness (pixels)
visualize: False # visualize results
augment: False # apply data augmentation to images
visualize: False # visualize model features
augment: False # apply image augmentation to prediction sources
agnostic_nms: False # class-agnostic NMS
retina_masks: False # use retina masks for object detection
retina_masks: False # use high-resolution segmentation masks
classes: null # filter results by class, i.e. class=0, or class=[0,2,3]
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # format to export to
@ -73,8 +74,8 @@ workspace: 4 # TensorRT: workspace size (GB)
nms: False # CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
lr0: 0.01 # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
@ -84,7 +85,7 @@ box: 7.5 # box loss gain
cls: 0.5 # cls loss gain (scale with pixels)
dfl: 1.5 # dfl loss gain
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
label_smoothing: 0.0
label_smoothing: 0.0 # label smoothing (fraction)
nbs: 64 # nominal batch size
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)

2
ultralytics/yolo/data/dataloaders/v5loader.py
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@ -615,7 +615,7 @@ class LoadImagesAndLabels(Dataset):
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
desc = f"{prefix}Scanning {path.parent / path.stem}..."
total = len(self.im_files)
with (Pool if total > 10000 else ThreadPool)(NUM_THREADS) as pool:
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix)))
pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT)
for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:

4
ultralytics/yolo/data/dataset.py
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@ -1,7 +1,7 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from multiprocessing.pool import ThreadPool
from pathlib import Path
import torchvision
@ -51,7 +51,7 @@ class YOLODataset(BaseDataset):
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
total = len(self.im_files)
with (Pool if total > 10000 else ThreadPool)(NUM_THREADS) as pool:
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(func=verify_image_label,
iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
repeat(self.use_keypoints)))

4
ultralytics/yolo/engine/exporter.py
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@ -67,7 +67,7 @@ import torch
import ultralytics
from ultralytics.nn.modules import Detect, Segment
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
from ultralytics.yolo.data.utils import check_dataset
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, get_default_args, yaml_save
@ -134,7 +134,7 @@ class Exporter:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)

10
ultralytics/yolo/engine/model.py
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@ -4,7 +4,7 @@ from pathlib import Path
from ultralytics import yolo # noqa
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
@ -136,7 +136,7 @@ class YOLO:
self.predictor = self.PredictorClass(overrides=overrides)
self.predictor.setup_model(model=self.model)
else: # only update args if predictor is already setup
self.predictor.args = get_config(self.predictor.args, overrides)
self.predictor.args = get_cfg(self.predictor.args, overrides)
return self.predictor(source=source, stream=stream, verbose=verbose)
@smart_inference_mode()
@ -151,7 +151,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
overrides["mode"] = "val"
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.data = data or args.data
args.task = self.task
@ -169,7 +169,7 @@ class YOLO:
overrides = self.overrides.copy()
overrides.update(kwargs)
args = get_config(config=DEFAULT_CFG_PATH, overrides=overrides)
args = get_cfg(cfg=DEFAULT_CFG_PATH, overrides=overrides)
args.task = self.task
print(args)
@ -201,7 +201,7 @@ class YOLO:
self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
self.model = self.trainer.model
self.trainer.train()
# update model and configs after training
# update model and cfg after training
self.model, _ = attempt_load_one_weight(str(self.trainer.best))
self.overrides = self.model.args

9
ultralytics/yolo/engine/predictor.py
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@ -33,7 +33,7 @@ from pathlib import Path
import cv2
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, callbacks, colorstr, ops
@ -70,7 +70,7 @@ class BasePredictor:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
name = self.args.name or f"{self.args.mode}"
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
@ -84,6 +84,7 @@ class BasePredictor:
self.bs = None
self.imgsz = None
self.device = None
self.classes = self.args.classes
self.dataset = None
self.vid_path, self.vid_writer = None, None
self.annotator = None
@ -100,7 +101,7 @@ class BasePredictor:
def write_results(self, results, batch, print_string):
raise NotImplementedError("print_results function needs to be implemented")
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_img, classes=None):
return preds
def setup_source(self, source=None):
@ -195,7 +196,7 @@ class BasePredictor:
# postprocess
with self.dt[2]:
results = self.postprocess(preds, im, im0s)
results = self.postprocess(preds, im, im0s, self.classes)
for i in range(len(im)):
p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
p = Path(p)

44
ultralytics/yolo/engine/results.py
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@ -21,6 +21,8 @@ class Results:
masks (Masks, optional): A Masks object containing the detection masks.
probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
orig_shape (tuple, optional): Original image size.
data (torch.Tensor): The raw masks tensor
"""
def __init__(self, boxes=None, masks=None, probs=None, orig_shape=None) -> None:
@ -81,19 +83,20 @@ class Results:
return len(getattr(self, item))
def __str__(self):
return self.__repr__()
str_out = ""
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__str__()
return str_out
def __repr__(self):
s = f'Ultralytics YOLO {self.__class__} instance\n' # string
if self.boxes is not None:
s = s + self.boxes.__repr__() + '\n'
if self.masks is not None:
s = s + self.masks.__repr__() + '\n'
if self.probs is not None:
s = s + self.probs.__repr__()
s += f'original size: {self.orig_shape}\n'
return s
str_out = ""
for item in self.comp:
if getattr(self, item) is None:
continue
str_out = str_out + getattr(self, item).__repr__()
return str_out
def __getattr__(self, attr):
name = self.__class__.__name__
@ -129,6 +132,7 @@ class Boxes:
xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
data (torch.Tensor): The raw bboxes tensor
"""
def __init__(self, boxes, orig_shape) -> None:
@ -198,15 +202,19 @@ class Boxes:
def shape(self):
return self.boxes.shape
@property
def data(self):
return self.boxes
def __len__(self): # override len(results)
return len(self.boxes)
def __str__(self):
return self.__repr__()
return self.boxes.__str__()
def __repr__(self):
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.boxes)}\n" +
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}")
f"shape: {self.boxes.shape}\n" + f"dtype: {self.boxes.dtype}\n + {self.boxes.__repr__()}")
def __getitem__(self, idx):
boxes = self.boxes[idx]
@ -257,12 +265,16 @@ class Masks:
def segments(self):
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
for x in reversed(ops.masks2segments(self.masks))]
for x in ops.masks2segments(self.masks)]
@property
def shape(self):
return self.masks.shape
@property
def data(self):
return self.masks
def cpu(self):
masks = self.masks.cpu()
return Masks(masks, self.orig_shape)
@ -283,11 +295,11 @@ class Masks:
return len(self.masks)
def __str__(self):
return self.__repr__()
return self.masks.__str__()
def __repr__(self):
return (f"Ultralytics YOLO {self.__class__} masks\n" + f"type: {type(self.masks)}\n" +
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}")
f"shape: {self.masks.shape}\n" + f"dtype: {self.masks.dtype}\n + {self.masks.__repr__()}")
def __getitem__(self, idx):
masks = self.masks[idx]

6
ultralytics/yolo/engine/trainer.py
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@ -23,7 +23,7 @@ from tqdm import tqdm
import ultralytics.yolo.utils as utils
from ultralytics import __version__
from ultralytics.nn.tasks import attempt_load_one_weight
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import (DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr,
yaml_save)
@ -79,7 +79,7 @@ class BaseTrainer:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
self.args = get_config(config, overrides)
self.args = get_cfg(config, overrides)
self.device = utils.torch_utils.select_device(self.args.device, self.args.batch)
self.check_resume()
self.console = LOGGER
@ -509,7 +509,7 @@ class BaseTrainer:
assert args_yaml.is_file(), \
FileNotFoundError('Resume checkpoint f{last} not found. '
'Please pass a valid checkpoint to resume from, i.e. yolo resume=path/to/last.pt')
args = get_config(args_yaml) # replace
args = get_cfg(args_yaml) # replace
args.model, resume = str(last), True # reinstate
self.args = args
self.resume = resume

4
ultralytics/yolo/engine/validator.py
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@ -8,7 +8,7 @@ import torch
from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.utils.checks import check_imgsz
@ -52,7 +52,7 @@ class BaseValidator:
self.dataloader = dataloader
self.pbar = pbar
self.logger = logger or LOGGER
self.args = args or get_config(DEFAULT_CFG_PATH)
self.args = args or get_cfg(DEFAULT_CFG_PATH)
self.model = None
self.data = None
self.device = None

2
ultralytics/yolo/utils/__init__.py
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@ -23,7 +23,7 @@ import yaml
# Constants
FILE = Path(__file__).resolve()
ROOT = FILE.parents[2] # YOLO
DEFAULT_CFG_PATH = ROOT / "yolo/configs/default.yaml"
DEFAULT_CFG_PATH = ROOT / "yolo/cfg/default.yaml"
RANK = int(os.getenv('RANK', -1))
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode

2
ultralytics/yolo/utils/callbacks/clearml.py
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@ -26,7 +26,7 @@ def on_pretrain_routine_start(trainer):
output_uri=True,
reuse_last_task_id=False,
auto_connect_frameworks={'pytorch': False})
task.connect(dict(trainer.args), name='General')
task.connect(vars(trainer.args), name='General')
def on_train_epoch_end(trainer):

2
ultralytics/yolo/utils/callbacks/comet.py
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@ -11,7 +11,7 @@ except (ModuleNotFoundError, ImportError):
def on_pretrain_routine_start(trainer):
experiment = comet_ml.Experiment(project_name=trainer.args.project or "YOLOv8")
experiment.log_parameters(dict(trainer.args))
experiment.log_parameters(vars(trainer.args))
def on_train_epoch_end(trainer):

3
ultralytics/yolo/utils/torch_utils.py
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@ -137,9 +137,10 @@ def model_info(model, verbose=False, imgsz=640):
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
flops = get_flops(model, imgsz)
fused = ' (fused)' if model.is_fused() else ''
fs = f', {flops:.1f} GFLOPs' if flops else ''
m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
LOGGER.info(f"{m} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
LOGGER.info(f"{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
def get_num_params(model):

2
ultralytics/yolo/v8/classify/predict.py
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@ -18,7 +18,7 @@ class ClassificationPredictor(BasePredictor):
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
return img
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_img, classes=None):
results = []
for i, pred in enumerate(preds):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape

5
ultralytics/yolo/v8/detect/predict.py
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@ -19,12 +19,13 @@ class DetectionPredictor(BasePredictor):
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_img, classes=None):
preds = ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det)
max_det=self.args.max_det,
classes=self.args.classes)
results = []
for i, pred in enumerate(preds):

5
ultralytics/yolo/v8/segment/predict.py
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@ -10,14 +10,15 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
class SegmentationPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_img, classes=None):
# TODO: filter by classes
p = ops.non_max_suppression(preds[0],
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
nm=32)
nm=32,
classes=self.args.classes)
results = []
proto = preds[1][-1]
for i, pred in enumerate(p):

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