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Threadpool fixes and CLI improvements (#550)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn Jocher
2023-01-22 17:08:08 +01:00
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docs/cfg.md
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@ -2,40 +2,51 @@ YOLO settings and hyperparameters play a critical role in the model's performanc
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.
YOLOv8 'yolo' CLI commands use the following syntax:
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.
!!! example ""
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.
=== "CLI"
```bash
yolo TASK MODE ARGS
```
### Setting the operation type
Where:
- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess
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](cfg.md) page and `defaults.yaml`
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
#### Tasks
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.
- **Detect**: 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
- **Segment**: 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
- **Classify**: 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.
#### Modes
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
- **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
- **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
- **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 |

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docs/cli.md
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@ -16,8 +16,9 @@ Where:
- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess
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](cfg.md) page.
- `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](cfg.md) page and `defaults.yaml`
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
!!! note ""

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docs/predict.md
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@ -1,74 +1,94 @@
Inference or prediction of a task returns a list of `Results` objects. Alternatively, in the streaming mode, it returns a generator of `Results` objects which is memory efficient. Streaming mode can be enabled by passing `stream=True` in predictor's call method.
Inference or prediction of a task returns a list of `Results` objects. Alternatively, in the streaming mode, it returns
a generator of `Results` objects which is memory efficient. Streaming mode can be enabled by passing `stream=True` in
predictor's call method.
!!! example "Predict"
=== "Getting a List"
```python
inputs = [img, img] # list of np arrays
results = model(inputs) # List of Results objects
for result in results:
boxes = result.boxes # Boxes object for bbox outputs
masks = result.masks # Masks object for segmenation masks outputs
probs = result.probs # Class probabilities for classification outputs
...
```
```python
inputs = [img, img] # list of np arrays
results = model(inputs) # List of Results objects
for result in results:
boxes = result.boxes # Boxes object for bbox outputs
masks = result.masks # Masks object for segmenation masks outputs
probs = result.probs # Class probabilities for classification outputs
```
=== "Getting a Generator"
```python
inputs = [img, img] # list of np arrays
results = model(inputs, stream=True) # Generator of Results objects
for result in results:
boxes = result.boxes # Boxes object for bbox outputs
masks = result.masks # Masks object for segmenation masks outputs
probs = result.probs # Class probabilities for classification outputs
...
```
```python
inputs = [img, img] # list of numpy arrays
results = model(inputs, stream=True) # generator of Results objects
for r in results:
boxes = r.boxes # Boxes object for bbox outputs
masks = r.masks # Masks object for segmenation masks outputs
probs = r.probs # Class probabilities for classification outputs
```
## Working with Results
Results object consists of these component objects:
- `results.boxes` : It is an object of class `Boxes`. It has properties and methods for manipulating bboxes
- `results.masks` : It is an object of class `Masks`. It can be used to index masks or to get segment coordinates.
- `results.prob` : It is a `Tensor` object. It contains the class probabilities/logits.
- `Results.boxes` : `Boxes` object with properties and methods for manipulating bboxes
- `Results.masks` : `Masks` object used to index masks or to get segment coordinates.
- `Results.prob` : `torch.Tensor` containing the class probabilities/logits.
Each result is composed of torch.Tensor by default, in which you can easily use following functionality:
```python
results = results.cuda()
results = results.cpu()
results = results.to("cpu")
results = results.numpy()
```
### Boxes
`Boxes` object can be used index, manipulate and convert bboxes to different formats. The box format conversion operations are cached, which means they're only calculated once per object and those values are reused for future calls.
`Boxes` object can be used index, manipulate and convert bboxes to different formats. The box format conversion
operations are cached, which means they're only calculated once per object and those values are reused for future calls.
- Indexing a `Boxes` objects returns a `Boxes` object
```python
boxes = results.boxes
box = boxes[0] # returns one box
results = model(inputs)
boxes = results[0].boxes
box = boxes[0] # returns one box
box.xyxy
```
- Properties and conversions
```
boxes.xyxy # box with xyxy format, (N, 4)
boxes.xywh # box with xywh format, (N, 4)
```python
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 .
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
masks = results.masks # Masks object
results = model(inputs)
masks = results[0].masks # Masks object
masks.segments # bounding coordinates of masks, List[segment] * N
masks.data # raw masks tensor, (N, H, W) or masks.masks
masks.data # raw masks tensor, (N, H, W) or masks.masks
```
### probs
`probs` attribute of `Results` class is a `Tensor` containing class probabilities of a classification operation.
```python
results.probs # cls prob, (num_class, )
results = model(inputs)
results[0].probs # cls prob, (num_class, )
```
Class reference documentation for `Results` module and its components can be found [here](reference/results.md)