ultralytics 8.0.53 DDP AMP and Edge TPU fixes (#1362)
Co-authored-by: Richard Aljaste <richardaljasteabramson@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Vuong Kha Sieu <75152429+hotfur@users.noreply.github.com>
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Glenn Jocher
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Object detection is a task that involves identifying the location and class of objects in an image or video stream.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class
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labels
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and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a
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scene, but don't need to know exactly where the object is or its exact shape.
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!!! tip "Tip"
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YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
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## Train
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Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
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the [Configuration](../cfg.md) page.
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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.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Train the model
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model.train(data="coco128.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Val
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
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training `data` and arguments as model attributes.
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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 model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.box.map # map50-95
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metrics.box.map50 # map50
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metrics.box.map75 # map75
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metrics.box.maps # a list contains map50-95 of each category
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```
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=== "CLI"
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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## Predict
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Use a trained YOLOv8n model to run predictions on images.
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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 model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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```
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=== "CLI"
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
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## Export
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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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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```
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Available YOLOv8 export formats include:
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| Format | `format=` | Model | Metadata |
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|--------------------------------------------------------------------|---------------|---------------------------|----------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |
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