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ultralytics 8.0.53 DDP AMP and Edge TPU fixes (#1362)

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Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
and segmenting them from the rest of the image.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
The output of an instance segmentation model is a set of masks or
contours that outline each object in the image, along with class labels and confidence scores for each object. Instance
segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
!!! tip "Tip"
YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8/seg){.md-button .md-button--primary}
## 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](../cfg.md) page.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.yaml") # build a new model from scratch
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
# Train the model
model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
```
## Val
Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
retains it's training `data` and arguments as model attributes.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.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(B)
metrics.box.map50 # map50(B)
metrics.box.map75 # map75(B)
metrics.box.maps # a list contains map50-95(B) of each category
metrics.seg.map # map50-95(M)
metrics.seg.map50 # map50(M)
metrics.seg.map75 # map75(M)
metrics.seg.maps # a list contains map50-95(M) of each category
```
=== "CLI"
```bash
yolo segment val model=yolov8n-seg.pt # val official model
yolo segment val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLOv8n-seg model to run predictions on images.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
=== "CLI"
```bash
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page.
## Export
Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolov8n-seg.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-seg export formats include:
| Format | `format=` | Model | Metadata |
|--------------------------------------------------------------------|---------------|-------------------------------|----------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` | ✅ |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlmodel` | ✅ |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ |