`ultralytics 8.0.117` NAS export, classify and tasks banner URL fixes (#3145)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

README.md

@@ -102,9 +102,9 @@ path = model.export(format="onnx")  # export the model to ONNX format
 
 ## <div align="center">Models</div>
 
-YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/modes/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
 
-<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
 
 All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 

README.zh-CN.md

@@ -102,9 +102,9 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 ## <div align="center">模型</div>
 
-在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/modes/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
+在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
 
-<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
 
 所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
 

docs/tasks/index.md

@@ -10,7 +10,7 @@ perform [detection](detect.md), [segmentation](segment.md), [classification](cla
 and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
 
 <br>
-<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
 
 ## [Detection](detect.md)
 

examples/tutorial.ipynb

@@ -548,7 +548,7 @@
         "\n",
         "YOLOv8 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
         "\n",
-        "<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png\">\n"
+        "<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\">\n"
       ],
       "metadata": {
         "id": "Phm9ccmOKye5"

mkdocs.yml

@@ -17,7 +17,7 @@ theme:
   icon:
     repo: fontawesome/brands/github
   font:
-    text: Roboto
+    text: Helvetica
     code: Roboto Mono
 
   palette:

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.116'
+__version__ = '8.0.117'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/yolo/engine/exporter.py

@@ -172,7 +172,8 @@ class Exporter:
 
         # Input
         im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
-        file = Path(getattr(model, 'pt_path', None) or getattr(model, 'yaml_file', None) or model.yaml['yaml_file'])
+        file = Path(
+            getattr(model, 'pt_path', None) or getattr(model, 'yaml_file', None) or model.yaml.get('yaml_file', ''))
         if file.suffix == '.yaml':
             file = Path(file.name)
 
@@ -207,7 +208,8 @@ class Exporter:
         self.im = im
         self.model = model
         self.file = file
-        self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
+        self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else \
+            tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
         self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
         trained_on = f'trained on {Path(self.args.data).name}' if self.args.data else '(untrained)'
         description = f'Ultralytics {self.pretty_name} model {trained_on}'

ultralytics/yolo/nas/model.py

@@ -1,6 +1,12 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 """
-# NAS model interface
+YOLO-NAS model interface.
+
+Usage - Predict:
+    from ultralytics import NAS
+
+    model = NAS('yolo_nas_s')
+    results = model.predict('ultralytics/assets/bus.jpg')
 """
 
 from pathlib import Path
@@ -33,11 +39,13 @@ class NAS:
         self.model.args = DEFAULT_CFG_DICT  # attach args to model
 
         # Standardize model
-        self.model.fuse = lambda verbose: self.model
+        self.model.fuse = lambda verbose=True: self.model
         self.model.stride = torch.tensor([32])
         self.model.names = dict(enumerate(self.model._class_names))
         self.model.is_fused = lambda: False  # for info()
         self.model.yaml = {}  # for info()
+        self.model.pt_path = model  # for export()
+        self.model.task = 'detect'  # for export()
         self.info()
 
     @smart_inference_mode()