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>

docs/tasks/classify.md

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+Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
+predefined classes.
+
+<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
+
+The output of an image classifier is a single class label and a confidence score. Image
+classification is useful when you need to know only what class an image belongs to and don't need to know where objects
+of that class are located or what their exact shape is.
+
+!!! tip "Tip"
+
+    YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8/cls){.md-button .md-button--primary}
+
+## Train
+
+Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments
+see the [Configuration](../usage/cfg.md) page.
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO("yolov8n-cls.yaml")  # build a new model from scratch
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+        
+        # Train the model
+        model.train(data="mnist160", epochs=100, imgsz=64)
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
+        ```
+
+## Val
+
+Validate trained YOLOv8n-cls model accuracy on the MNIST160 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-cls.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.top1   # top1 accuracy
+        metrics.top5   # top5 accuracy
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo classify val model=yolov8n-cls.pt  # val official model
+        yolo classify val model=path/to/best.pt  # val custom model
+        ```
+
+## Predict
+
+Use a trained YOLOv8n-cls model to run predictions on images.
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO("yolov8n-cls.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 classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
+        yolo classify 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-cls model to a different format like ONNX, CoreML, etc.
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO("yolov8n-cls.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-cls.pt format=onnx  # export official model
+        yolo export model=path/to/best.pt format=onnx  # export custom trained model
+        ```
+
+Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models,
+i.e. `yolo predict model=yolov8n-cls.onnx`.
+
+| Format                                                             | `format` Argument | Model                         | Metadata |
+|--------------------------------------------------------------------|-------------------|-------------------------------|----------|
+| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n-cls.pt`              | ✅        |
+| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-cls.torchscript`     | ✅        |
+| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-cls.onnx`            | ✅        |
+| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n-cls_openvino_model/` | ✅        |
+| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-cls.engine`          | ✅        |
+| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-cls.mlmodel`         | ✅        |
+| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-cls_saved_model/`    | ✅        |
+| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n-cls.pb`              | ❌        |
+| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n-cls.tflite`          | ✅        |
+| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n-cls_edgetpu.tflite`  | ✅        |
+| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n-cls_web_model/`      | ✅        |
+| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n-cls_paddle_model/`   | ✅        |
+