`ultralytics 8.0.88` Pose TFLite model fix (#2261)

docs/index.md

@@ -31,6 +31,6 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf), launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors and spatial pyramid pooling.
 - [YOLOv4](https://arxiv.org/abs/2004.10934) was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function.
 - [YOLOv5](https://github.com/ultralytics/yolov5) further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats.
-- [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/en) in 2022 and is in use in many of the company's autonomous delivery robots.
+- [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 - [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
 - [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

mkdocs.yml

@@ -127,7 +127,7 @@ plugins:
 # Primary navigation
 nav:
   - Home:
-      - index.md
+      - Home: index.md
       - Quickstart: quickstart.md
       - Modes:
           - modes/index.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.87'
+__version__ = '8.0.88'
 
 from ultralytics.hub import start
 from ultralytics.yolo.engine.model import YOLO

ultralytics/hub/utils.py

@@ -161,7 +161,7 @@ class Events:
         Initializes the Events object with default values for events, rate_limit, and metadata.
         """
         self.events = []  # events list
-        self.rate_limit = 10.0  # rate limit (seconds)
+        self.rate_limit = 60.0  # rate limit (seconds)
         self.t = 0.0  # rate limit timer (seconds)
         self.metadata = {
             'cli': Path(sys.argv[0]).name == 'yolo',
@@ -204,7 +204,9 @@ class Events:
 
         # Time is over rate limiter, send now
         data = {'client_id': SETTINGS['uuid'], 'events': self.events}  # SHA-256 anonymized UUID hash and events list
-        smart_request('post', self.url, json=data, retry=0, code=3)  # equivalent to requests.post(self.url, json=data)
+
+        # POST equivalent to requests.post(self.url, json=data)
+        smart_request('post', self.url, json=data, retry=0, verbose=False)
 
         # Reset events and rate limit timer
         self.events = []

ultralytics/nn/modules.py

@@ -541,12 +541,19 @@ class Pose(Detect):
         x = self.detect(self, x)
         if self.training:
             return x, kpt
-        pred_kpt = self.kpts_decode(kpt)
+        pred_kpt = self.kpts_decode(bs, kpt)
         return torch.cat([x, pred_kpt], 1) if self.export else (torch.cat([x[0], pred_kpt], 1), (x[1], kpt))
 
-    def kpts_decode(self, kpts):
+    def kpts_decode(self, bs, kpts):
         """Decodes keypoints."""
         ndim = self.kpt_shape[1]
+        if self.export:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
+            y = kpts.view(bs, *self.kpt_shape, -1)
+            a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
+            if ndim == 3:
+                a = torch.cat((a, y[:, :, 1:2].sigmoid()), 2)
+            return a.view(bs, self.nk, -1)
+        else:
             y = kpts.clone()
             if ndim == 3:
                 y[:, 2::3].sigmoid_()  # inplace sigmoid