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42
README.md
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@ -123,11 +123,11 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>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<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo mode=val task=detect data=coco.yaml device=0`
<br>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.
<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
<br>Reproduce by `yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu`
</details>
@ -157,16 +157,16 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset.
<br>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.
<br>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.
<br>Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
<br>Reproduce by `yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu`
</details>

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@ -117,11 +117,11 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
| 模型 | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** 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。
<br>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
<details><summary>实例分割</summary>
| 模型 | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(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<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** 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。
<br>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
<details><summary>分类</summary>
| 模型 | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(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<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(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 |
- **mAP<sup>val</sup>** 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。
<br>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。
<br>复现命令 `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`

2
mkdocs.yml
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@ -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

2
ultralytics/__init__.py
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@ -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

2
ultralytics/nn/autobackend.py
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@ -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')

12
ultralytics/nn/modules.py
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@ -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

6
ultralytics/nn/tasks.py
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@ -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,

10
ultralytics/yolo/data/augment.py
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@ -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

6
ultralytics/yolo/data/dataloaders/stream_loaders.py
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@ -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()

6
ultralytics/yolo/engine/exporter.py
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@ -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):

8
ultralytics/yolo/utils/__init__.py
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@ -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

4
ultralytics/yolo/utils/checks.py
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@ -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())

4
ultralytics/yolo/utils/ops.py
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@ -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

2
ultralytics/yolo/utils/plotting.py
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@ -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

2
ultralytics/yolo/utils/torch_utils.py
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@ -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)

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