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ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-18 20:47:21 -07:00 • committed by GitHub
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2
docs/en/modes/benchmark.md
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@ -76,7 +76,7 @@ Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` prov
| Key | Default Value | Description |
|-----------|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
| `model` | `None` | Specifies the path to the model file. Accepts both `.pt` and `.yaml` formats, e.g., `"yolov8n.pt"` for pre-trained models or configuration files. |
| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for validation data. Example: `"coco128.yaml"`. |
| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for validation data. Example: `"coco8.yaml"`. |
| `imgsz` | `640` | The input image size for the model. Can be a single integer for square images or a tuple `(width, height)` for non-square, e.g., `(640, 480)`. |
| `half` | `False` | Enables FP16 (half-precision) inference, reducing memory usage and possibly increasing speed on compatible hardware. Use `half=True` to enable. |
| `int8` | `False` | Activates INT8 quantization for further optimized performance on supported devices, especially useful for edge devices. Set `int8=True` to use. |

20
docs/en/modes/train.md
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@ -47,7 +47,7 @@ The following are some notable features of YOLOv8's Train mode:
## Usage Examples
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device=cpu` will be used. See Arguments section below for a full list of training arguments.
Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device=cpu` will be used. See Arguments section below for a full list of training arguments.
!!! Example "Single-GPU and CPU Training Example"
@ -64,20 +64,20 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. The train
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
```
### Multi-GPU Training
@ -97,14 +97,14 @@ Multi-GPU training allows for more efficient utilization of available hardware r
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device=[0, 1])
results = model.train(data='coco8.yaml', epochs=100, imgsz=640, device=[0, 1])
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model using GPUs 0 and 1
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=0,1
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640 device=0,1
```
### Apple M1 and M2 MPS Training
@ -124,14 +124,14 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model with 2 GPUs
results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device='mps')
results = model.train(data='coco8.yaml', epochs=100, imgsz=640, device='mps')
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model using GPUs 0 and 1
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=mps
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640 device=mps
```
While leveraging the computational power of the M1/M2 chips, this enables more efficient processing of the training tasks. For more detailed guidance and advanced configuration options, please refer to the [PyTorch MPS documentation](https://pytorch.org/docs/stable/notes/mps.html).
@ -178,7 +178,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
| Argument | Default | Description |
|-------------------|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `model` | `None` | Specifies the model file for training. Accepts a path to either a `.pt` pretrained model or a `.yaml` configuration file. Essential for defining the model structure or initializing weights. |
| `data` | `None` | Path to the dataset configuration file (e.g., `coco128.yaml`). This file contains dataset-specific parameters, including paths to training and validation data, class names, and number of classes. |
| `data` | `None` | Path to the dataset configuration file (e.g., `coco8.yaml`). This file contains dataset-specific parameters, including paths to training and validation data, class names, and number of classes. |
| `epochs` | `100` | Total number of training epochs. Each epoch represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance. |
| `time` | `None` | Maximum training time in hours. If set, this overrides the `epochs` argument, allowing training to automatically stop after the specified duration. Useful for time-constrained training scenarios. |
| `patience` | `100` | Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus. |

34
docs/en/modes/val.md
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@ -47,7 +47,7 @@ These are the notable functionalities offered by YOLOv8's Val mode:
## Usage Examples
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
!!! Example
@ -79,22 +79,22 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
When validating YOLO models, several arguments can be fine-tuned to optimize the evaluation process. These arguments control aspects such as input image size, batch processing, and performance thresholds. Below is a detailed breakdown of each argument to help you customize your validation settings effectively.
| Argument | Type | Default | Description |
|---------------|---------|---------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco128.yaml`). This file includes paths to validation data, class names, and number of classes. |
| `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. |
| `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. |
| `save_json` | `bool` | `False` | If `True`, saves the results to a JSON file for further analysis or integration with other tools. |
| `save_hybrid` | `bool` | `False` | If `True`, saves a hybrid version of labels that combines original annotations with additional model predictions. |
| `conf` | `float` | `0.001` | Sets the minimum confidence threshold for detections. Detections with confidence below this threshold are discarded. |
| `iou` | `float` | `0.6` | Sets the Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Helps in reducing duplicate detections. |
| `max_det` | `int` | `300` | Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections. |
| `half` | `bool` | `True` | Enables half-precision (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on accuracy. |
| `device` | `str` | `None` | Specifies the device for validation (`cpu`, `cuda:0`, etc.). Allows flexibility in utilizing CPU or GPU resources. |
| `dnn` | `bool` | `False` | If `True`, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods. |
| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. |
| `rect` | `bool` | `False` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. |
| `split` | `str` | `val` | Determines the dataset split to use for validation (`val`, `test`, or `train`). Allows flexibility in choosing the data segment for performance evaluation. |
| Argument | Type | Default | Description |
|---------------|---------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco8.yaml`). This file includes paths to validation data, class names, and number of classes. |
| `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. |
| `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. |
| `save_json` | `bool` | `False` | If `True`, saves the results to a JSON file for further analysis or integration with other tools. |
| `save_hybrid` | `bool` | `False` | If `True`, saves a hybrid version of labels that combines original annotations with additional model predictions. |
| `conf` | `float` | `0.001` | Sets the minimum confidence threshold for detections. Detections with confidence below this threshold are discarded. |
| `iou` | `float` | `0.6` | Sets the Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Helps in reducing duplicate detections. |
| `max_det` | `int` | `300` | Limits the maximum number of detections per image. Useful in dense scenes to prevent excessive detections. |
| `half` | `bool` | `True` | Enables half-precision (FP16) computation, reducing memory usage and potentially increasing speed with minimal impact on accuracy. |
| `device` | `str` | `None` | Specifies the device for validation (`cpu`, `cuda:0`, etc.). Allows flexibility in utilizing CPU or GPU resources. |
| `dnn` | `bool` | `False` | If `True`, uses the OpenCV DNN module for ONNX model inference, offering an alternative to PyTorch inference methods. |
| `plots` | `bool` | `False` | When set to `True`, generates and saves plots of predictions versus ground truth for visual evaluation of the model's performance. |
| `rect` | `bool` | `False` | If `True`, uses rectangular inference for batching, reducing padding and potentially increasing speed and efficiency. |
| `split` | `str` | `val` | Determines the dataset split to use for validation (`val`, `test`, or `train`). Allows flexibility in choosing the data segment for performance evaluation. |
Each of these settings plays a vital role in the validation process, allowing for a customizable and efficient evaluation of YOLO models. Adjusting these parameters according to your specific needs and resources can help achieve the best balance between accuracy and performance.