Merge branch 'main' into dependabot/pip/duckdb-lte-0.10.0

docs/en/models/yolov10.md

@@ -202,20 +202,20 @@ The YOLOv10 models series offers a range of models, each optimized for high-perf
 
 Due to the new operations introduced with YOLOv10, not all export formats provided by Ultralytics are currently supported. The following table outlines which formats have been successfully converted using Ultralytics for YOLOv10. Feel free to open a pull request if you're able to [provide a contribution change](../help/contributing.md) for adding export support of additional formats for YOLOv10.
 
-| Export Format                                     | Supported |
+| Export Format                                     | Export Support | Exported Model Inference | Notes                                       |
-| ------------------------------------------------- | --------- |
+| ------------------------------------------------- | -------------- | ------------------------ | ------------------------------------------- |
-| [TorchScript](../integrations/torchscript.md)     | ✅        |
+| [TorchScript](../integrations/torchscript.md)     | ✅             | ✅                       | Standard PyTorch model format.              |
-| [ONNX](../integrations/onnx.md)                   | ✅        |
+| [ONNX](../integrations/onnx.md)                   | ✅             | ✅                       | Widely supported for deployment.            |
-| [OpenVINO](../integrations/openvino.md)           | ✅        |
+| [OpenVINO](../integrations/openvino.md)           | ✅             | ✅                       | Optimized for Intel hardware.               |
-| [TensorRT](../integrations/tensorrt.md)           | ✅        |
+| [TensorRT](../integrations/tensorrt.md)           | ✅             | ✅                       | Optimized for NVIDIA GPUs.                  |
-| [CoreML](../integrations/coreml.md)               | ❌        |
+| [CoreML](../integrations/coreml.md)               | ✅             | ✅                       | Limited to Apple devices.                   |
-| [TF SavedModel](../integrations/tf-savedmodel.md) | ✅        |
+| [TF SavedModel](../integrations/tf-savedmodel.md) | ✅             | ✅                       | TensorFlow's standard model format.         |
-| [TF GraphDef](../integrations/tf-graphdef.md)     | ✅        |
+| [TF GraphDef](../integrations/tf-graphdef.md)     | ✅             | ✅                       | Legacy TensorFlow format.                   |
-| [TF Lite](../integrations/tflite.md)              | ✅        |
+| [TF Lite](../integrations/tflite.md)              | ✅             | ✅                       | Optimized for mobile and embedded.          |
-| [TF Edge TPU](../integrations/edge-tpu.md)        | ❌        |
+| [TF Edge TPU](../integrations/edge-tpu.md)        | ✅             | ✅                       | Specific to Google's Edge TPU devices.      |
-| [TF.js](../integrations/tfjs.md)                  | ❌        |
+| [TF.js](../integrations/tfjs.md)                  | ✅             | ✅                       | JavaScript environment for browser use.     |
-| [PaddlePaddle](../integrations/paddlepaddle.md)   | ❌        |
+| [PaddlePaddle](../integrations/paddlepaddle.md)   | ❌             | ❌                       | Popular in China; less global support.      |
-| [NCNN](../integrations/ncnn.md)                   | ❌        |
+| [NCNN](../integrations/ncnn.md)                   | ✅             | ❌                       | Layer `torch.topk` not exists or registered |
 
 ## Conclusion
 

examples/YOLOv8-ONNXRuntime-CPP/inference.cpp

@@ -221,8 +221,8 @@ char* YOLO_V8::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::
     case YOLO_DETECT_V8:
     case YOLO_DETECT_V8_HALF:
     {
-        int strideNum = outputNodeDims[1];//8400
+        int signalResultNum = outputNodeDims[1];//84
-        int signalResultNum = outputNodeDims[2];//84
+        int strideNum = outputNodeDims[2];//8400
         std::vector<int> class_ids;
         std::vector<float> confidences;
         std::vector<cv::Rect> boxes;
@@ -230,18 +230,18 @@ char* YOLO_V8::TensorProcess(clock_t& starttime_1, cv::Mat& iImg, N& blob, std::
         if (modelType == YOLO_DETECT_V8)
         {
             // FP32
-            rawData = cv::Mat(strideNum, signalResultNum, CV_32F, output);
+            rawData = cv::Mat(signalResultNum, strideNum, CV_32F, output);
         }
         else
         {
             // FP16
-            rawData = cv::Mat(strideNum, signalResultNum, CV_16F, output);
+            rawData = cv::Mat(signalResultNum, strideNum, CV_16F, output);
             rawData.convertTo(rawData, CV_32F);
         }
         //Note:
         //ultralytics add transpose operator to the output of yolov8 model.which make yolov8/v5/v7 has same shape
         //https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
-        //rowData = rowData.t();
+        rawData = rawData.t();
 
         float* data = (float*)rawData.data;
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.81"
+__version__ = "8.2.82"
 
 import os
 

ultralytics/nn/modules/head.py

@@ -8,7 +8,6 @@ import torch
 import torch.nn as nn
 from torch.nn.init import constant_, xavier_uniform_
 
-from ultralytics.utils import MACOS
 from ultralytics.utils.tal import TORCH_1_10, dist2bbox, dist2rbox, make_anchors
 
 from .block import DFL, BNContrastiveHead, ContrastiveHead, Proto
@@ -133,38 +132,26 @@ class Detect(nn.Module):
     @staticmethod
     def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80):
         """
-        Post-processes the predictions obtained from a YOLOv10 model.
+        Post-processes YOLO model predictions.
 
         Args:
-            preds (torch.Tensor): The predictions obtained from the model. It should have a shape of (batch_size, num_boxes, 4 + num_classes).
+            preds (torch.Tensor): Raw predictions with shape (batch_size, num_anchors, 4 + nc) with last dimension
-            max_det (int): The maximum number of detections to keep.
+                format [x, y, w, h, class_probs].
-            nc (int, optional): The number of classes. Defaults to 80.
+            max_det (int): Maximum detections per image.
+            nc (int, optional): Number of classes. Default: 80.
 
         Returns:
-            (torch.Tensor): The post-processed predictions with shape (batch_size, max_det, 6),
+            (torch.Tensor): Processed predictions with shape (batch_size, min(max_det, num_anchors), 6) and last
-                including bounding boxes, scores and cls.
+                dimension format [x, y, w, h, max_class_prob, class_index].
         """
-        assert 4 + nc == preds.shape[-1]
+        batch_size, anchors, predictions = preds.shape  # i.e. shape(16,8400,84)
         boxes, scores = preds.split([4, nc], dim=-1)
-        max_scores = scores.amax(dim=-1)
+        index = scores.amax(dim=-1).topk(min(max_det, anchors))[1].unsqueeze(-1)
-        max_scores, index = torch.topk(max_scores, min(max_det, max_scores.shape[1]), axis=-1)
+        boxes = boxes.gather(dim=1, index=index.repeat(1, 1, 4))
-        index = index.unsqueeze(-1)
+        scores = scores.gather(dim=1, index=index.repeat(1, 1, nc))
-        boxes = torch.gather(boxes, dim=1, index=index.repeat(1, 1, boxes.shape[-1]))
+        scores, index = scores.flatten(1).topk(max_det)
-        scores = torch.gather(scores, dim=1, index=index.repeat(1, 1, scores.shape[-1]))
+        i = torch.arange(batch_size)[..., None]  # batch indices
-
+        return torch.cat([boxes[i, index // nc], scores[..., None], (index % nc)[..., None].float()], dim=-1)
-        # NOTE: simplify result but slightly lower mAP
-        # scores, labels = scores.max(dim=-1)
-        # return torch.cat([boxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
-
-        scores, index = torch.topk(scores.flatten(1), max_det, axis=-1)
-        labels = index % nc
-        index = index // nc
-        # Set int64 dtype for MPS and CoreML compatibility to avoid 'gather_along_axis' ops error
-        if MACOS:
-            index = index.to(torch.int64)
-        boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
-
-        return torch.cat([boxes, scores.unsqueeze(-1), labels.unsqueeze(-1).to(boxes.dtype)], dim=-1)
 
 
 class Segment(Detect):

ultralytics/utils/benchmarks.py

@@ -97,20 +97,17 @@ def benchmark(
                 assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
                 assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
                 assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
-                assert not is_end2end, "End-to-end models not supported by CoreML and TF.js yet"
             if i in {3, 5}:  # CoreML and OpenVINO
                 assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
             if i in {6, 7, 8}:  # TF SavedModel, TF GraphDef, and TFLite
                 assert not isinstance(model, YOLOWorld), "YOLOWorldv2 TensorFlow exports not supported by onnx2tf yet"
             if i in {9, 10}:  # TF EdgeTPU and TF.js
                 assert not isinstance(model, YOLOWorld), "YOLOWorldv2 TensorFlow exports not supported by onnx2tf yet"
-                assert not is_end2end, "End-to-end models not supported by TF EdgeTPU and TF.js yet"
             if i in {11}:  # Paddle
                 assert not isinstance(model, YOLOWorld), "YOLOWorldv2 Paddle exports not supported yet"
                 assert not is_end2end, "End-to-end models not supported by PaddlePaddle yet"
             if i in {12}:  # NCNN
                 assert not isinstance(model, YOLOWorld), "YOLOWorldv2 NCNN exports not supported yet"
-                assert not is_end2end, "End-to-end models not supported by NCNN yet"
             if "cpu" in device.type:
                 assert cpu, "inference not supported on CPU"
             if "cuda" in device.type:
@@ -130,6 +127,8 @@ def benchmark(
             assert model.task != "pose" or i != 7, "GraphDef Pose inference is not supported"
             assert i not in {9, 10}, "inference not supported"  # Edge TPU and TF.js are unsupported
             assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13"  # CoreML
+            if i in {12}:
+                assert not is_end2end, "End-to-end torch.topk operation is not supported for NCNN prediction yet"
             exported_model.predict(ASSETS / "bus.jpg", imgsz=imgsz, device=device, half=half)
 
             # Validate