`ultralytics 8.2.55` adaptive `tflite_support` logic (#14385)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Nguyễn Anh Bình <sometimesocrazy@gmail.com>
Co-authored-by: Johnny <johnnynuca14@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.54"
+__version__ = "8.2.55"
 
 import os
 

ultralytics/engine/exporter.py

@@ -1017,13 +1017,13 @@ class Exporter:
         """Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata."""
         import flatbuffers
 
-        if ARM64:
-            from tflite_support import metadata  # noqa
-            from tflite_support import metadata_schema_py_generated as schema  # noqa
-        else:
+        try:
             # TFLite Support bug https://github.com/tensorflow/tflite-support/issues/954#issuecomment-2108570845
             from tensorflow_lite_support.metadata import metadata_schema_py_generated as schema  # noqa
             from tensorflow_lite_support.metadata.python import metadata  # noqa
+        except ImportError:  # ARM64 systems may not have the 'tensorflow_lite_support' package available
+            from tflite_support import metadata  # noqa
+            from tflite_support import metadata_schema_py_generated as schema  # noqa
 
         # Create model info
         model_meta = schema.ModelMetadataT()

ultralytics/hub/utils.py

@@ -185,7 +185,7 @@ class Events:
     def __init__(self):
         """Initializes the Events object with default values for events, rate_limit, and metadata."""
         self.events = []  # events list
-        self.rate_limit = 60.0  # rate limit (seconds)
+        self.rate_limit = 30.0  # rate limit (seconds)
         self.t = 0.0  # rate limit timer (seconds)
         self.metadata = {
             "cli": Path(ARGV[0]).name == "yolo",

ultralytics/utils/torch_utils.py

@@ -271,7 +271,7 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
     fs = f", {flops:.1f} GFLOPs" if flops else ""
     yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
     model_name = Path(yaml_file).stem.replace("yolo", "YOLO") or "Model"
-    LOGGER.info(f"{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}")
+    LOGGER.info(f"{model_name} summary{fused}: {n_l:,} layers, {n_p:,} parameters, {n_g:,} gradients{fs}")
     return n_l, n_p, n_g, flops
 
 
@@ -513,6 +513,9 @@ def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
         for f in Path('path/to/model/checkpoints').rglob('*.pt'):
             strip_optimizer(f)
         ```
+
+    Note:
+        Use `ultralytics.nn.torch_safe_load` for missing modules with `x = torch_safe_load(f)[0]`
     """
     try:
         x = torch.load(f, map_location=torch.device("cpu"))