`ultralytics 8.0.108` add Meituan YOLOv6 models (#2811)

Co-authored-by: Michael Currie <mcurrie@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Hicham Talaoubrid <98521878+HichTala@users.noreply.github.com>
Co-authored-by: Zlobin Vladimir <vladimir.zlobin@intel.com>
Co-authored-by: Szymon Mikler <sjmikler@gmail.com>

README.md

@@ -93,7 +93,7 @@ model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 model.train(data="coco128.yaml", epochs=3)  # train the model
 metrics = model.val()  # evaluate model performance on the validation set
 results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
-success = model.export(format="onnx")  # export the model to ONNX format
+path = model.export(format="onnx")  # export the model to ONNX format
 ```
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.

docker/Dockerfile

@@ -29,7 +29,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e . albumentations comet tensorboard thop
+RUN pip install --no-cache -e . albumentations comet tensorboard thop pycocotools
 
 # Set environment variables
 ENV OMP_NUM_THREADS=1

docs/models/index.md

@@ -11,6 +11,7 @@ In this documentation, we provide information on four major models:
 
 1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
 2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
+3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
 4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
 5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.

docs/models/sam.md

@@ -26,7 +26,7 @@ For more information about the Segment Anything Model and the SA-1B dataset, ple
 SAM can be used for a variety of downstream tasks involving object and image distributions beyond its training data. Examples include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. By employing prompt engineering, SAM can adapt to new tasks and data distributions in a zero-shot manner, making it a versatile and powerful tool for image segmentation tasks.
 
 ```python
-from ultralytics.vit import SAM
+from ultralytics import SAM
 
 model = SAM('sam_b.pt')
 model.info()  # display model information

docs/models/yolov6.md

@@ -0,0 +1,81 @@
+---
+comments: true
+description: Discover Meituan YOLOv6, a robust real-time object detector. Learn how to utilize pre-trained models with Ultralytics Python API for a variety of tasks.
+---
+
+# Meituan YOLOv6
+
+## Overview
+
+[Meituan](https://about.meituan.com/) YOLOv6 is a cutting-edge object detector that offers remarkable balance between speed and accuracy, making it a popular choice for real-time applications. This model introduces several notable enhancements on its architecture and training scheme, including the implementation of a Bi-directional Concatenation (BiC) module, an anchor-aided training (AAT) strategy, and an improved backbone and neck design for state-of-the-art accuracy on the COCO dataset.
+
+![Meituan YOLOv6](https://user-images.githubusercontent.com/26833433/240750495-4da954ce-8b3b-41c4-8afd-ddb74361d3c2.png)
+![Model example image](https://user-images.githubusercontent.com/26833433/240750557-3e9ec4f0-0598-49a8-83ea-f33c91eb6d68.png)
+**Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The
+structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
+
+### Key Features
+
+- **Bi-directional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
+- **Anchor-Aided Training (AAT) Strategy:** This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
+- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
+- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
+
+## Pre-trained Models
+
+YOLOv6 provides various pre-trained models with different scales:
+
+- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
+- YOLOv6-S: 45.0% AP at 484 FPS.
+- YOLOv6-M: 50.0% AP at 226 FPS.
+- YOLOv6-L: 52.8% AP at 116 FPS.
+- YOLOv6-L6: State-of-the-art accuracy in real-time.
+
+YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
+
+## Usage
+
+### Python API
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolov6n.yaml")  # build new model from scratch
+model.info()  # display model information
+model.predict("path/to/image.jpg")  # predict
+```
+
+### Supported Tasks
+
+| Model Type | Pre-trained Weights | Tasks Supported  |
+|------------|---------------------|------------------|
+| YOLOv6-N   | `yolov6-n.pt`       | Object Detection |
+| YOLOv6-S   | `yolov6-s.pt`       | Object Detection |
+| YOLOv6-M   | `yolov6-m.pt`       | Object Detection |
+| YOLOv6-L   | `yolov6-l.pt`       | Object Detection |
+| YOLOv6-L6  | `yolov6-l6.pt`      | Object Detection |
+
+## Supported Modes
+
+| Mode       | Supported          |
+|------------|--------------------|
+| Inference  | :heavy_check_mark: |
+| Validation | :heavy_check_mark: |
+| Training   | :heavy_check_mark: |
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
+
+```bibtex
+@misc{li2023yolov6,
+      title={YOLOv6 v3.0: A Full-Scale Reloading}, 
+      author={Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
+      year={2023},
+      eprint={2301.05586},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/usage/hyperparameter_tuning.md

@@ -50,14 +50,14 @@ To install the required packages, run:
 
 The `tune()` method in YOLOv8 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:
 
-| Parameter       | Type           | Description                                                                                                                                                                                                                                                                                                                   | Default Value |
+| Parameter       | Type           | Description                                                                                                                                                                                                                                                                                   | Default Value |
-|-----------------|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
+|-----------------|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
-| `data`          | str            | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings.                                                                                                                                             |               |
+| `data`          | str            | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings.                                                                                                             |               |
-| `space`         | dict, optional | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters.                                                    |               |
+| `space`         | dict, optional | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters.                    |               |
-| `grace_period`  | int, optional  | The grace period in epochs for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api_docs/schedulers.html#asha-tune-schedulers-asha) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10            |
+| `grace_period`  | int, optional  | The grace period in epochs for the [ASHA scheduler]https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10            |
-| `gpu_per_trial` | int, optional  | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs.                                                                                                                                          | None          |
+| `gpu_per_trial` | int, optional  | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs.                                                                                                          | None          |
-| `max_samples`   | int, optional  | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.                                                                                                                                | 10            |
+| `max_samples`   | int, optional  | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.                                                                                                | 10            |
-| `train_args`    | dict, optional | A dictionary of additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations.                                                                                                          | {}            |
+| `train_args`    | dict, optional | A dictionary of additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations.                                                                          | {}            |
 
 By customizing these parameters, you can fine-tune the hyperparameter optimization process to suit your specific needs and available computational resources.
 

mkdocs.yml

@@ -163,6 +163,7 @@ nav:
       - models/index.md
       - YOLOv3: models/yolov3.md
       - YOLOv5: models/yolov5.md
+      - YOLOv6: models/yolov6.md
       - YOLOv8: models/yolov8.md
       - SAM (Segment Anything Model): models/sam.md
       - RT-DETR (Realtime Detection Transformer): models/rtdetr.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.107'
+__version__ = '8.0.108'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/models/v6/yolov6.yaml

@@ -0,0 +1,51 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+act: nn.ReLU()
+nc: 80  # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [ 0.33, 0.25, 1024 ]
+  s: [ 0.33, 0.50, 1024 ]
+  m: [ 0.67, 0.75, 768 ]
+  l: [ 1.00, 1.00, 512 ]
+  x: [ 1.00, 1.25, 512 ]
+
+# YOLOv6-3.0s backbone
+backbone:
+  # [from, repeats, module, args]
+  - [ -1, 1, Conv, [ 64, 3, 2 ] ]  # 0-P1/2
+  - [ -1, 1, Conv, [ 128, 3, 2 ] ]  # 1-P2/4
+  - [ -1, 6, Conv, [ 128, 3, 1 ] ]
+  - [ -1, 1, Conv, [ 256, 3, 2 ] ]  # 3-P3/8
+  - [ -1, 12, Conv, [ 256, 3, 1 ] ]
+  - [ -1, 1, Conv, [ 512, 3, 2 ] ]  # 5-P4/16
+  - [ -1, 18, Conv, [ 512, 3, 1 ] ]
+  - [ -1, 1, Conv, [ 1024, 3, 2 ] ]  # 7-P5/32
+  - [ -1, 9, Conv, [ 1024, 3, 1 ] ]
+  - [ -1, 1, SPPF, [ 1024, 5 ] ]  # 9
+
+# YOLOv6-3.0s head
+head:
+  - [ -1, 1, nn.ConvTranspose2d, [ 256, 2, 2, 0 ] ]
+  - [ [ -1, 6 ], 1, Concat, [ 1 ] ]  # cat backbone P4
+  - [ -1, 1, Conv, [ 256, 3, 1 ] ]
+  - [ -1, 9, Conv, [ 256, 3, 1 ] ]  # 13
+
+  - [ -1, 1, nn.ConvTranspose2d, [ 128, 2, 2, 0 ] ]
+  - [ [ -1, 4 ], 1, Concat, [ 1 ] ]  # cat backbone P3
+  - [ -1, 1, Conv, [ 128, 3, 1 ] ]
+  - [ -1, 9, Conv, [ 128, 3, 1 ] ]  # 17
+
+  - [ -1, 1, Conv, [ 128, 3, 2 ] ]
+  - [ [ -1, 12 ], 1, Concat, [ 1 ] ]  # cat head P4
+  - [ -1, 1, Conv, [ 256, 3, 1 ] ]
+  - [ -1, 9, Conv, [ 256, 3, 1 ] ]  # 21
+
+  - [ -1, 1, Conv, [ 256, 3, 2 ] ]
+  - [ [ -1, 9 ], 1, Concat, [ 1 ] ]  # cat head P5
+  - [ -1, 1, Conv, [ 512, 3, 1 ] ]
+  - [ -1, 9, Conv, [ 512, 3, 1 ] ]  # 25
+
+  - [ [ 17, 21, 25 ], 1, Detect, [ nc ] ]  # Detect(P3, P4, P5)

ultralytics/vit/sam/model.py

@@ -2,6 +2,7 @@
 
 from ultralytics.yolo.cfg import get_cfg
 
+from ...yolo.utils.torch_utils import model_info
 from .build import build_sam
 from .predict import Predictor
 
@@ -33,3 +34,13 @@ class SAM:
     def val(self, **kwargs):
         """Run validation given dataset."""
         raise NotImplementedError("SAM models don't support validation")
+
+    def info(self, detailed=False, verbose=True):
+        """
+        Logs model info.
+
+        Args:
+            detailed (bool): Show detailed information about model.
+            verbose (bool): Controls verbosity.
+        """
+        return model_info(self.model, detailed=detailed, verbose=verbose)

ultralytics/yolo/cfg/__init__.py

@@ -111,10 +111,13 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
         check_cfg_mismatch(cfg, overrides)
         cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)
 
-    # Special handling for numeric project/names
+    # Special handling for numeric project/name
     for k in 'project', 'name':
         if k in cfg and isinstance(cfg[k], (int, float)):
             cfg[k] = str(cfg[k])
+    if cfg.get('name') == 'model':  # assign model to 'name' arg
+        cfg['name'] = cfg.get('model', '').split('.')[0]
+        LOGGER.warning(f"WARNING ⚠️ 'name=model' automatically updated to 'name={cfg['name']}'.")
 
     # Type and Value checks
     for k, v in cfg.items():

ultralytics/yolo/data/build.py

@@ -116,7 +116,7 @@ def check_source(source):
         is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
         is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://'))
         webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
-        screenshot = source.lower().startswith('screen')
+        screenshot = source.lower() == 'screen'
         if is_url and is_file:
             source = check_file(source)  # download
     elif isinstance(source, tuple(LOADERS)):

ultralytics/yolo/engine/model.py

@@ -331,12 +331,12 @@ class YOLO:
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         overrides['mode'] = 'export'
+        if overrides.get('imgsz') is None:
+            overrides['imgsz'] = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
+        if overrides.get('batch') is None:
+            overrides['batch'] = 1  # default to 1 if not modified
         args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
         args.task = self.task
-        if args.imgsz == DEFAULT_CFG.imgsz:
-            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
-        if args.batch == DEFAULT_CFG.batch:
-            args.batch = 1  # default to 1 if not modified
         return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
 
     def train(self, **kwargs):

ultralytics/yolo/engine/trainer.py

@@ -684,12 +684,17 @@ def check_amp(model):
     im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
     prefix = colorstr('AMP: ')
     LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
+    warning_msg = "Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False."
     try:
         from ultralytics import YOLO
         assert amp_allclose(YOLO('yolov8n.pt'), im)
         LOGGER.info(f'{prefix}checks passed ✅')
     except ConnectionError:
-        LOGGER.warning(f"{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. Setting 'amp=True'.")
+        LOGGER.warning(f'{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. {warning_msg}')
+    except (AttributeError, ModuleNotFoundError):
+        LOGGER.warning(
+            f'{prefix}checks skipped ⚠️. Unable to load YOLOv8n due to possible Ultralytics package modifications. {warning_msg}'
+        )
     except AssertionError:
         LOGGER.warning(f'{prefix}checks failed ❌. Anomalies were detected with AMP on your system that may lead to '
                        f'NaN losses or zero-mAP results, so AMP will be disabled during training.')

ultralytics/yolo/utils/__init__.py

@@ -372,12 +372,15 @@ def is_online() -> bool:
     """
     import socket
 
-    for server in '1.1.1.1', '8.8.8.8', '223.5.5.5':  # Cloudflare, Google, AliDNS:
+    for host in '1.1.1.1', '8.8.8.8', '223.5.5.5':  # Cloudflare, Google, AliDNS:
         try:
-            socket.create_connection((server, 53), timeout=2)  # connect to (server, port=53)
+            test_connection = socket.create_connection(address=(host, 53), timeout=2)
-            return True
         except (socket.timeout, socket.gaierror, OSError):
             continue
+        else:
+            # If the connection was successful, close it to avoid a ResourceWarning
+            test_connection.close()
+            return True
     return False
 
 

ultralytics/yolo/utils/benchmarks.py

@@ -3,7 +3,7 @@
 Benchmark a YOLO model formats for speed and accuracy
 
 Usage:
-    from ultralytics.yolo.utils.benchmarks import ProfileModels, run_benchmarks
+    from ultralytics.yolo.utils.benchmarks import ProfileModels, benchmark
     ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])
     run_benchmarks(model='yolov8n.pt', imgsz=160)
 
@@ -163,7 +163,7 @@ class ProfileModels:
         profile(): Profiles the models and prints the result.
     """
 
-    def __init__(self, paths: list, num_timed_runs=100, num_warmup_runs=3, imgsz=640, trt=True):
+    def __init__(self, paths: list, num_timed_runs=100, num_warmup_runs=10, imgsz=640, trt=True):
         self.paths = paths
         self.num_timed_runs = num_timed_runs
         self.num_warmup_runs = num_warmup_runs
@@ -181,22 +181,22 @@ class ProfileModels:
         table_rows = []
         device = 0 if torch.cuda.is_available() else 'cpu'
         for file in files:
-            engine_file = ''
+            engine_file = file.with_suffix('.engine')
             if file.suffix in ('.pt', '.yaml'):
                 model = YOLO(str(file))
-                num_params, num_flops = model.info()
+                model_info = model.info()
-                if self.trt and device == 0:
+                if self.trt and device == 0 and not engine_file.is_file():
                     engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=device)
                 onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=device)
             elif file.suffix == '.onnx':
-                num_params, num_flops = self.get_onnx_model_info(file)
+                model_info = self.get_onnx_model_info(file)
                 onnx_file = file
             else:
                 continue
 
             t_engine = self.profile_tensorrt_model(str(engine_file))
             t_onnx = self.profile_onnx_model(str(onnx_file))
-            table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, num_params, num_flops))
+            table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info))
 
         self.print_table(table_rows)
 
@@ -216,10 +216,21 @@ class ProfileModels:
         return [Path(file) for file in sorted(files)]
 
     def get_onnx_model_info(self, onnx_file: str):
-        return 0.0, 0.0
+        # return (num_layers, num_params, num_gradients, num_flops)
+        return 0.0, 0.0, 0.0, 0.0
+
+    def iterative_sigma_clipping(self, data, sigma=2, max_iters=5):
+        data = np.array(data)
+        for _ in range(max_iters):
+            mean, std = np.mean(data), np.std(data)
+            clipped_data = data[(data > mean - sigma * std) & (data < mean + sigma * std)]
+            if len(clipped_data) == len(data):
+                break
+            data = clipped_data
+        return data
 
     def profile_tensorrt_model(self, engine_file: str):
-        if not Path(engine_file).is_file():
+        if not self.trt or not Path(engine_file).is_file():
             return 0.0, 0.0
 
         # Warmup runs
@@ -230,10 +241,11 @@ class ProfileModels:
 
         # Timed runs
         run_times = []
-        for _ in tqdm(range(self.num_timed_runs), desc=engine_file):
+        for _ in tqdm(range(self.num_timed_runs * 30), desc=engine_file):
             results = model(input_data, verbose=False)
             run_times.append(results[0].speed['inference'])  # Convert to milliseconds
 
+        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
         return np.mean(run_times), np.std(run_times)
 
     def profile_onnx_model(self, onnx_file: str):
@@ -246,7 +258,23 @@ class ProfileModels:
         sess = ort.InferenceSession(onnx_file, sess_options, providers=['CPUExecutionProvider'])
 
         input_tensor = sess.get_inputs()[0]
-        input_data = np.random.rand(*input_tensor.shape).astype(np.float16 if torch.cuda.is_available() else np.float32)
+        input_type = input_tensor.type
+
+        # Mapping ONNX datatype to numpy datatype
+        if 'float16' in input_type:
+            input_dtype = np.float16
+        elif 'float' in input_type:
+            input_dtype = np.float32
+        elif 'double' in input_type:
+            input_dtype = np.float64
+        elif 'int64' in input_type:
+            input_dtype = np.int64
+        elif 'int32' in input_type:
+            input_dtype = np.int32
+        else:
+            raise ValueError(f'Unsupported ONNX datatype {input_type}')
+
+        input_data = np.random.rand(*input_tensor.shape).astype(input_dtype)
         input_name = input_tensor.name
         output_name = sess.get_outputs()[0].name
 
@@ -261,17 +289,19 @@ class ProfileModels:
             sess.run([output_name], {input_name: input_data})
             run_times.append((time.time() - start_time) * 1000)  # Convert to milliseconds
 
+        run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping
         return np.mean(run_times), np.std(run_times)
 
-    def generate_table_row(self, model_name, t_onnx, t_engine, num_params, num_flops):
+    def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
-        return f'| {model_name} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {num_params / 1e6:.1f} | {num_flops:.1f} |'
+        layers, params, gradients, flops = model_info
+        return f'| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |'
 
     def print_table(self, table_rows):
         gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU'
         header = f'| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |'
         separator = '|-------------|---------------------|--------------------|------------------------------|-----------------------------------|------------------|-----------------|'
 
-        print(header)
+        print(f'\n\n{header}')
         print(separator)
         for row in table_rows:
             print(row)

ultralytics/yolo/utils/ops.py

@@ -104,7 +104,8 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
     """
     if ratio_pad is None:  # calculate from img0_shape
         gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
+        pad = round((img1_shape[1] - img0_shape[1] * gain) / 2 - 0.1), round(
+            (img1_shape[0] - img0_shape[0] * gain) / 2 - 0.1)  # wh padding
     else:
         gain = ratio_pad[0][0]
         pad = ratio_pad[1]

ultralytics/yolo/utils/torch_utils.py

@@ -162,8 +162,9 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
     """Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]."""
     if not verbose:
         return
-    n_p = get_num_params(model)
+    n_p = get_num_params(model)  # number of parameters
-    n_g = get_num_gradients(model)  # number gradients
+    n_g = get_num_gradients(model)  # number of gradients
+    n_l = len(list(model.modules()))  # number of layers
     if detailed:
         LOGGER.info(
             f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
@@ -173,11 +174,12 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
                         (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype))
 
     flops = get_flops(model, imgsz)
-    fused = ' (fused)' if model.is_fused() else ''
+    fused = ' (fused)' if getattr(model, 'is_fused', lambda: False)() else ''
     fs = f', {flops:.1f} GFLOPs' if flops else ''
-    m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
+    yaml_file = getattr(model, 'yaml_file', '') or getattr(model, 'yaml', {}).get('yaml_file', '')
-    LOGGER.info(f'{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}')
+    model_name = Path(yaml_file).stem.replace('yolo', 'YOLO') or 'Model'
-    return n_p, flops
+    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
 
 
 def get_num_params(model):
@@ -199,8 +201,7 @@ def get_flops(model, imgsz=640):
         im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
         flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 if thop else 0  # stride GFLOPs
         imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
-        flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
+        return flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
-        return flops
     except Exception:
         return 0