ultralytics 8.0.141 create new SettingsManager (#3790)
This commit is contained in:
Glenn Jocher
GitHub
@ -25,15 +25,15 @@ full list of export arguments.
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!!! example ""
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=== "Python"
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```python
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from ultralytics.utils.benchmarks import benchmark
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# Benchmark on GPU
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benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
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```
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=== "CLI"
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```bash
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yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
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```
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@ -23,19 +23,19 @@ export arguments.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format='onnx')
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```
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=== "CLI"
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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@ -85,4 +85,4 @@ i.e. `format='onnx'` or `format='engine'`.
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |
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@ -65,4 +65,4 @@ or `accuracy_top5` metrics (for classification), and the inference time in milli
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formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
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their specific use case based on their requirements for speed and accuracy.
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[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}
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[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}
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@ -21,7 +21,7 @@ passing `stream=True` in the predictor's call method.
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# Run batched inference on a list of images
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results = model(['im1.jpg', 'im2.jpg']) # return a list of Results objects
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# Process results list
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for result in results:
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boxes = result.boxes # Boxes object for bbox outputs
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@ -39,7 +39,7 @@ passing `stream=True` in the predictor's call method.
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# Run batched inference on a list of images
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results = model(['im1.jpg', 'im2.jpg'], stream=True) # return a generator of Results objects
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# Process results generator
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for result in results:
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boxes = result.boxes # Boxes object for bbox outputs
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@ -65,7 +65,7 @@ YOLOv8 can process different types of input sources for inference, as shown in t
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| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
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| numpy | `np.zeros((640,1280,3))` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
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| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` of `float32 (0.0-1.0)` | BCHW format with RGB channels. |
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| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
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| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
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| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. |
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| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. |
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| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. |
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@ -77,204 +77,204 @@ Below are code examples for using each source type:
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!!! example "Prediction sources"
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=== "image"
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Run inference on an image file.
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Run inference on an image file.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define path to the image file
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source = 'path/to/image.jpg'
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "screenshot"
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Run inference on the current screen content as a screenshot.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define current screenshot as source
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source = 'screen'
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "URL"
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Run inference on an image or video hosted remotely via URL.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define remote image or video URL
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source = 'https://ultralytics.com/images/bus.jpg'
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "PIL"
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Run inference on an image opened with Python Imaging Library (PIL).
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```python
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from PIL import Image
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Open an image using PIL
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source = Image.open('path/to/image.jpg')
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "OpenCV"
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Run inference on an image read with OpenCV.
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```python
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import cv2
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Read an image using OpenCV
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source = cv2.imread('path/to/image.jpg')
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "numpy"
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Run inference on an image represented as a numpy array.
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```python
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import numpy as np
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8
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source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype='uint8')
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "torch"
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Run inference on an image represented as a PyTorch tensor.
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```python
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import torch
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
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source = torch.rand(1, 3, 640, 640, dtype=torch.float32)
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "CSV"
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Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
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```python
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import torch
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define a path to a CSV file with images, URLs, videos and directories
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source = 'path/to/file.csv'
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# Run inference on the source
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results = model(source) # list of Results objects
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```
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=== "video"
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Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define path to video file
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source = 'path/to/video.mp4'
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# Run inference on the source
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results = model(source, stream=True) # generator of Results objects
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```
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=== "directory"
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Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define path to directory containing images and videos for inference
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source = 'path/to/dir'
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# Run inference on the source
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results = model(source, stream=True) # generator of Results objects
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```
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=== "glob"
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Run inference on all images and videos that match a glob expression with `*` characters.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define a glob search for all JPG files in a directory
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source = 'path/to/dir/*.jpg'
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# OR define a recursive glob search for all JPG files including subdirectories
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source = 'path/to/dir/**/*.jpg'
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# Run inference on the source
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results = model(source, stream=True) # generator of Results objects
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```
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=== "YouTube"
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Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define source as YouTube video URL
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source = 'https://youtu.be/Zgi9g1ksQHc'
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# Run inference on the source
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results = model(source, stream=True) # generator of Results objects
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```
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=== "Stream"
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Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols.
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```python
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from ultralytics import YOLO
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# Load a pretrained YOLOv8n model
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model = YOLO('yolov8n.pt')
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# Define source as RTSP, RTMP or IP streaming address
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source = 'rtsp://example.com/media.mp4'
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# Run inference on the source
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results = model(source, stream=True) # generator of Results objects
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```
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@ -417,7 +417,7 @@ operations are cached, meaning they're only calculated once per object, and thos
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masks = results[0].masks # Masks object
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masks.xy # x, y segments (pixels), List[segment] * N
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masks.xyn # x, y segments (normalized), List[segment] * N
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masks.data # raw masks tensor, (N, H, W) or masks.masks
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masks.data # raw masks tensor, (N, H, W) or masks.masks
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```
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### Keypoints
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@ -432,7 +432,7 @@ operations are cached, meaning they're only calculated once per object, and thos
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keypoints.xy # x, y keypoints (pixels), (num_dets, num_kpts, 2/3), the last dimension can be 2 or 3, depends the model.
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keypoints.xyn # x, y keypoints (normalized), (num_dets, num_kpts, 2/3)
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keypoints.conf # confidence score(num_dets, num_kpts) of each keypoint if the last dimension is 3.
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keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
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keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
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```
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### probs
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@ -448,7 +448,7 @@ operations are cached, meaning they're only calculated once per object, and thos
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probs.top1 # The top1 indices of classification, a value with Int type.
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probs.top5conf # The top5 scores of classification, a tensor with shape (5, ).
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probs.top1conf # The top1 scores of classification. a value with torch.tensor type.
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keypoints.data # raw probs tensor, (num_class, )
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keypoints.data # raw probs tensor, (num_class, )
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```
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Class reference documentation for `Results` module and its components can be found [here](../reference/engine/results.md)
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@ -489,37 +489,37 @@ Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video f
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```python
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import cv2
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from ultralytics import YOLO
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# Load the YOLOv8 model
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model = YOLO('yolov8n.pt')
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# Open the video file
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video_path = "path/to/your/video/file.mp4"
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cap = cv2.VideoCapture(video_path)
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# Loop through the video frames
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while cap.isOpened():
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# Read a frame from the video
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success, frame = cap.read()
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if success:
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# Run YOLOv8 inference on the frame
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results = model(frame)
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# Visualize the results on the frame
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annotated_frame = results[0].plot()
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# Display the annotated frame
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cv2.imshow("YOLOv8 Inference", annotated_frame)
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# Break the loop if 'q' is pressed
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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else:
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# Break the loop if the end of the video is reached
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break
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# Release the video capture object and close the display window
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cap.release()
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cv2.destroyAllWindows()
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```
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```
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@ -27,21 +27,21 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n.pt') # load an official detection model
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model = YOLO('yolov8n-seg.pt') # load an official segmentation model
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model = YOLO('path/to/best.pt') # load a custom model
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# Track with the model
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")
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```
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=== "CLI"
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```bash
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yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" # official detection model
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yolo track model=yolov8n-seg.pt source=... # official segmentation model
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@ -62,15 +62,15 @@ to [predict page](https://docs.ultralytics.com/modes/predict/).
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
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```
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=== "CLI"
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```bash
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yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
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@ -84,18 +84,18 @@ any configurations(expect the `tracker_type`) you need to.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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model = YOLO('yolov8n.pt')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
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results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
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```
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=== "CLI"
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```bash
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yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml'
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```
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Please refer to [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers)
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page
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page
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@ -21,20 +21,20 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Argum
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||||
Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.yaml') # build a new model from YAML
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
|
||||
|
||||
|
||||
# Train the model
|
||||
model.train(data='coco128.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
|
||||
@ -53,18 +53,18 @@ The training device can be specified using the `device` argument. If no argument
|
||||
!!! example "Multi-GPU Training Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model with 2 GPUs
|
||||
model.train(data='coco128.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
|
||||
@ -79,18 +79,18 @@ To enable training on Apple M1 and M2 chips, you should specify 'mps' as your de
|
||||
!!! example "MPS Training Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
||||
|
||||
|
||||
# Train the model with 2 GPUs
|
||||
model.train(data='coco128.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
|
||||
@ -111,18 +111,18 @@ Below is an example of how to resume an interrupted training using Python and vi
|
||||
!!! example "Resume Training Example"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('path/to/last.pt') # load a partially trained model
|
||||
|
||||
|
||||
# Resume training
|
||||
model.train(resume=True)
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
|
||||
```bash
|
||||
# Resume an interrupted training
|
||||
yolo train resume model=path/to/last.pt
|
||||
@ -239,4 +239,4 @@ tensorboard --logdir ultralytics/runs # replace with 'runs' directory
|
||||
|
||||
This will load TensorBoard and direct it to the directory where your training logs are saved.
|
||||
|
||||
After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
|
||||
After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
|
||||
|
||||
@ -19,14 +19,14 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
|
||||
!!! example ""
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a model
|
||||
model = YOLO('yolov8n.pt') # load an official model
|
||||
model = YOLO('path/to/best.pt') # load a custom model
|
||||
|
||||
|
||||
# Validate the model
|
||||
metrics = model.val() # no arguments needed, dataset and settings remembered
|
||||
metrics.box.map # map50-95
|
||||
@ -35,7 +35,7 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
|
||||
metrics.box.maps # a list contains map50-95 of each category
|
||||
```
|
||||
=== "CLI"
|
||||
|
||||
|
||||
```bash
|
||||
yolo detect val model=yolov8n.pt # val official model
|
||||
yolo detect val model=path/to/best.pt # val custom model
|
||||
@ -61,4 +61,4 @@ Validation settings for YOLO models refer to the various hyperparameters and con
|
||||
| `plots` | `False` | show plots during training |
|
||||
| `rect` | `False` | rectangular val with each batch collated for minimum padding |
|
||||
| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
|
||||
|
|
||||
|
|
||||
|
||||
Reference in New Issue
Block a user