diff --git a/README.md b/README.md index 5b03295..0f66d62 100644 --- a/README.md +++ b/README.md @@ -123,11 +123,11 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | - | 0.99 | 3.2 | 8.7 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | - | 1.20 | 11.2 | 28.6 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | - | 1.83 | 25.9 | 78.9 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | - | 2.39 | 43.7 | 165.2 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | - | 3.53 | 68.2 | 257.8 | +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` @@ -140,16 +140,16 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u | Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) | | ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 | +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.11 | 1.21 | 3.4 | 12.6 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. -
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` +
Reproduce by `yolo mode=val task=segment data=coco.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. -
Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu` +
Reproduce by `yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu` @@ -157,16 +157,16 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(B) at 640 | | ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 | - -- **mAPval** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset. -
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | + +- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. +
Reproduce by `yolo mode=val task=classify data=path/to/ImageNet device=0` - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. -
Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu` +
Reproduce by `yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu` diff --git a/README.zh-CN.md b/README.zh-CN.md index 506126e..1b538f5 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -117,11 +117,11 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX | 模型 | 尺寸
（像素） | mAPval
50-95 | 推理速度
CPU ONNX
(ms) | 推理速度
A100 TensorRT
(ms) | 参数量
(M) | FLOPs
(B) | | ------------------------------------------------------------------------------------ | --------------- | -------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | - | 0.99 | 3.2 | 8.7 | -| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | - | 1.20 | 11.2 | 28.6 | -| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | - | 1.83 | 25.9 | 78.9 | -| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | - | 2.39 | 43.7 | 165.2 | -| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | - | 3.53 | 68.2 | 257.8 | +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 | - **mAPval** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
复现命令 `yolo mode=val task=detect data=coco.yaml device=0` @@ -132,13 +132,13 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
实例分割 -| 模型 | 尺寸
（像素） | mAPbox
50-95 | mAPmask
50-95 | 推理速度
CPU ONNX
(ms) | 推理速度
A100 TensorRT
(ms) | 参数量
(M) | FLOPs
(B) | -| --------------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- | -| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 | -| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 | -| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 | -| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 | -| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 | +| 模型 | 尺寸
（像素） | mAPbox
50-95 | mAPmask
50-95 | 推理速度
CPU ONNX
(ms) | 推理速度
A100 TensorRT
(ms) | 参数量
(M) | FLOPs
(B) | +| ---------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- | +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.11 | 1.21 | 3.4 | 12.6 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | - **mAPval** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
复现命令 `yolo mode=val task=detect data=coco.yaml device=0` @@ -149,15 +149,15 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
分类 -| 模型 | 尺寸
（像素） | acc
top1 | acc
top5 | 推理速度
CPU ONNX
(ms) | 推理速度
A100 TensorRT
(ms) | 参数量
(M) | FLOPs
(B) at 640 | -| --------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | --------------- | ------------------------ | -| [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 | -| [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 | -| [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 | -| [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 | -| [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 | +| 模型 | 尺寸
（像素） | acc
top1 | acc
top5 | 推理速度
CPU ONNX
(ms) | 推理速度
A100 TensorRT
(ms) | 参数量
(M) | FLOPs
(B) at 640 | +| ---------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | --------------- | ------------------------ | +| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | +| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | +| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | +| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | +| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | -- **mAPval** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。 +- **acc** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。
复现命令 `yolo mode=val task=detect data=coco.yaml device=0` - **推理速度**使用 ImageNet 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
复现命令 `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu` diff --git a/mkdocs.yml b/mkdocs.yml index b7d358c..4084814 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -13,12 +13,14 @@ theme: palette: # Palette toggle for light mode - scheme: default + primary: grey toggle: icon: material/brightness-7 name: Switch to dark mode # Palette toggle for dark mode - scheme: slate + primary: black toggle: icon: material/brightness-4 name: Switch to light mode diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 660ab44..7d690f7 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, GPL-3.0 license -__version__ = "8.0.3" +__version__ = "8.0.4" from ultralytics.hub import checks from ultralytics.yolo.engine.model import YOLO diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py index 4d862ba..661a6fc 100644 --- a/ultralytics/nn/autobackend.py +++ b/ultralytics/nn/autobackend.py @@ -197,7 +197,7 @@ class AutoBackend(nn.Module): input_details = interpreter.get_input_details() # inputs output_details = interpreter.get_output_details() # outputs elif tfjs: # TF.js - raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') + raise NotImplementedError('ERROR: YOLOv8 TF.js inference is not supported') elif paddle: # PaddlePaddle LOGGER.info(f'Loading {w} for PaddlePaddle inference...') check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') diff --git a/ultralytics/nn/modules.py b/ultralytics/nn/modules.py index 5782969..2d3435f 100644 --- a/ultralytics/nn/modules.py +++ b/ultralytics/nn/modules.py @@ -366,7 +366,7 @@ class Concat(nn.Module): class AutoShape(nn.Module): - # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + # YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold agnostic = False # NMS class-agnostic @@ -465,7 +465,7 @@ class AutoShape(nn.Module): class Detections: - # YOLOv5 detections class for inference results + # YOLOv8 detections class for inference results def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): super().__init__() d = pred[0].device # device @@ -572,7 +572,7 @@ class Detections: return self._run(pprint=True) # print results def __repr__(self): - return f'YOLOv5 {self.__class__} instance\n' + self.__str__() + return f'YOLOv8 {self.__class__} instance\n' + self.__str__() class Proto(nn.Module): @@ -603,7 +603,7 @@ class Ensemble(nn.ModuleList): # heads class Detect(nn.Module): - # YOLOv5 Detect head for detection models + # YOLOv8 Detect head for detection models dynamic = False # force grid reconstruction export = False # export mode shape = None @@ -650,7 +650,7 @@ class Detect(nn.Module): class Segment(Detect): - # YOLOv5 Segment head for segmentation models + # YOLOv8 Segment head for segmentation models def __init__(self, nc=80, nm=32, npr=256, ch=()): super().__init__(nc, ch) self.nm = nm # number of masks @@ -673,7 +673,7 @@ class Segment(Detect): class Classify(nn.Module): - # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2) + # YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2) def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() c_ = 1280 # efficientnet_b0 size diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py index a7d0118..b676294 100644 --- a/ultralytics/nn/tasks.py +++ b/ultralytics/nn/tasks.py @@ -142,7 +142,7 @@ class BaseModel(nn.Module): class DetectionModel(BaseModel): - # YOLOv5 detection model + # YOLOv8 detection model def __init__(self, cfg='yolov8n.yaml', ch=3, nc=None, verbose=True): # model, input channels, number of classes super().__init__() self.yaml = cfg if isinstance(cfg, dict) else yaml_load(check_yaml(cfg), append_filename=True) # cfg dict @@ -222,13 +222,13 @@ class DetectionModel(BaseModel): class SegmentationModel(DetectionModel): - # YOLOv5 segmentation model + # YOLOv8 segmentation model def __init__(self, cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True): super().__init__(cfg, ch, nc, verbose) class ClassificationModel(BaseModel): - # YOLOv5 classification model + # YOLOv8 classification model def __init__(self, cfg=None, model=None, diff --git a/ultralytics/yolo/data/augment.py b/ultralytics/yolo/data/augment.py index d5414da..1b5b85e 100644 --- a/ultralytics/yolo/data/augment.py +++ b/ultralytics/yolo/data/augment.py @@ -531,7 +531,7 @@ class CopyPaste: class Albumentations: - # YOLOv5 Albumentations class (optional, only used if package is installed) + # YOLOv8 Albumentations class (optional, only used if package is installed) def __init__(self, p=1.0): self.p = p self.transform = None @@ -699,7 +699,7 @@ def classify_albumentations( std=IMAGENET_STD, auto_aug=False, ): - # YOLOv5 classification Albumentations (optional, only used if package is installed) + # YOLOv8 classification Albumentations (optional, only used if package is installed) prefix = colorstr("albumentations: ") try: import albumentations as A @@ -732,7 +732,7 @@ def classify_albumentations( class ClassifyLetterBox: - # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + # YOLOv8 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) def __init__(self, size=(640, 640), auto=False, stride=32): super().__init__() self.h, self.w = (size, size) if isinstance(size, int) else size @@ -751,7 +751,7 @@ class ClassifyLetterBox: class CenterCrop: - # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) + # YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) def __init__(self, size=640): super().__init__() self.h, self.w = (size, size) if isinstance(size, int) else size @@ -764,7 +764,7 @@ class CenterCrop: class ToTensor: - # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + # YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) def __init__(self, half=False): super().__init__() self.half = half diff --git a/ultralytics/yolo/data/dataloaders/stream_loaders.py b/ultralytics/yolo/data/dataloaders/stream_loaders.py index 6365cb0..5d2373c 100644 --- a/ultralytics/yolo/data/dataloaders/stream_loaders.py +++ b/ultralytics/yolo/data/dataloaders/stream_loaders.py @@ -19,7 +19,7 @@ from ultralytics.yolo.utils.checks import check_requirements class LoadStreams: - # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` + # YOLOv8 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` def __init__(self, sources='file.streams', imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1): torch.backends.cudnn.benchmark = True # faster for fixed-size inference self.mode = 'stream' @@ -105,7 +105,7 @@ class LoadStreams: class LoadScreenshots: - # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` + # YOLOv8 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None): # source = [screen_number left top width height] (pixels) check_requirements('mss') @@ -154,7 +154,7 @@ class LoadScreenshots: class LoadImages: - # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` + # YOLOv8 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` def __init__(self, path, imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1): if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line path = Path(path).read_text().rsplit() diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py index c318859..869a375 100644 --- a/ultralytics/yolo/engine/exporter.py +++ b/ultralytics/yolo/engine/exporter.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, GPL-3.0 license """ -Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit +Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit Format | `format=argument` | Model --- | --- | --- @@ -81,7 +81,7 @@ MACOS = platform.system() == 'Darwin' # macOS environment def export_formats(): - # YOLOv5 export formats + # YOLOv8 export formats x = [ ['PyTorch', '-', '.pt', True, True], ['TorchScript', 'torchscript', '.torchscript', True, True], @@ -99,7 +99,7 @@ def export_formats(): def try_export(inner_func): - # YOLOv5 export decorator, i..e @try_export + # YOLOv8 export decorator, i..e @try_export inner_args = get_default_args(inner_func) def outer_func(*args, **kwargs): diff --git a/ultralytics/yolo/utils/__init__.py b/ultralytics/yolo/utils/__init__.py index e6747c5..9e8417f 100644 --- a/ultralytics/yolo/utils/__init__.py +++ b/ultralytics/yolo/utils/__init__.py @@ -24,11 +24,11 @@ ROOT = FILE.parents[2] # YOLO DEFAULT_CONFIG = ROOT / "yolo/configs/default.yaml" RANK = int(os.getenv('RANK', -1)) NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads -AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode +AUTOINSTALL = str(os.getenv('YOLO_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf -VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode +VERBOSE = str(os.getenv('YOLO_VERBOSE', True)).lower() == 'true' # global verbose mode TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format -LOGGING_NAME = 'yolov5' +LOGGING_NAME = 'ultralytics' HELP_MSG = \ """ Usage examples for running YOLOv8: @@ -288,7 +288,7 @@ def set_logging(name=LOGGING_NAME, verbose=True): class TryExcept(contextlib.ContextDecorator): - # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager + # YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager def __init__(self, msg=''): self.msg = msg diff --git a/ultralytics/yolo/utils/checks.py b/ultralytics/yolo/utils/checks.py index 4c5ad23..ef388c6 100644 --- a/ultralytics/yolo/utils/checks.py +++ b/ultralytics/yolo/utils/checks.py @@ -92,7 +92,7 @@ def check_version(current: str = "0.0.0", from pkg_resources import parse_version current, minimum = (parse_version(x) for x in (current, minimum)) result = (current == minimum) if pinned else (current >= minimum) # bool - warning_message = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" + warning_message = f"WARNING ⚠️ {name}{minimum} is required by YOLOv8, but {name}{current} is currently installed" if hard: assert result, emojis(warning_message) # assert min requirements met if verbose and not result: @@ -176,7 +176,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=() n += 1 if s and install and AUTOINSTALL: # check environment variable - LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...") + LOGGER.info(f"{prefix} YOLOv8 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...") try: assert check_online(), "AutoUpdate skipped (offline)" LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode()) diff --git a/ultralytics/yolo/utils/ops.py b/ultralytics/yolo/utils/ops.py index 677df60..c24e6ab 100644 --- a/ultralytics/yolo/utils/ops.py +++ b/ultralytics/yolo/utils/ops.py @@ -15,7 +15,7 @@ from .metrics import box_iou class Profile(contextlib.ContextDecorator): - # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager + # YOLOv8 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager def __init__(self, t=0.0): self.t = t self.cuda = torch.cuda.is_available() @@ -139,7 +139,7 @@ def non_max_suppression( # Checks assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' - if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out) prediction = prediction[0] # select only inference output device = prediction.device diff --git a/ultralytics/yolo/utils/plotting.py b/ultralytics/yolo/utils/plotting.py index f03a22a..d317af0 100644 --- a/ultralytics/yolo/utils/plotting.py +++ b/ultralytics/yolo/utils/plotting.py @@ -41,7 +41,7 @@ colors = Colors() # create instance for 'from utils.plots import colors' class Annotator: - # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + # YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic diff --git a/ultralytics/yolo/utils/torch_utils.py b/ultralytics/yolo/utils/torch_utils.py index 52a9d17..13b24f0 100644 --- a/ultralytics/yolo/utils/torch_utils.py +++ b/ultralytics/yolo/utils/torch_utils.py @@ -319,7 +319,7 @@ def guess_task_from_head(head): def profile(input, ops, n=10, device=None): - """ YOLOv5 speed/memory/FLOPs profiler + """ YOLOv8 speed/memory/FLOPs profiler Usage: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x)