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README.md
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@ -254,18 +254,18 @@ YOLOv5 is designed for simplicity and ease of use. We prioritize real-world perf
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This table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.
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| Model | Size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| Model | Size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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<details>
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@ -90,9 +90,6 @@ pip install -r requirements.txt
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```python
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import torch
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from PIL import Image
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import cv2
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import numpy as np
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# 加载 YOLOv5 模型（选项：yolov5n, yolov5s, yolov5m, yolov5l, yolov5x）
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model = torch.hub.load("ultralytics/yolov5", "yolov5s") # 默认：yolov5s
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@ -222,8 +219,8 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
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</div>
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| Ultralytics HUB 🚀 | W&B | Comet ⭐ 新增 | Neural Magic |
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| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
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| Ultralytics HUB 🚀 | W&B | Comet ⭐ 新增 | Neural Magic |
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| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: |
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| 简化 YOLO 工作流程：使用 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 无缝跟踪实验、超参数和结果。 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 让您保存 YOLOv5 模型、恢复训练，并交互式地可视化和调试预测。 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 在 CPU 上运行 YOLOv5 推理速度提高达 6 倍。 |
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## ⭐ Ultralytics HUB
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@ -257,19 +254,19 @@ YOLOv5 的设计注重简单性和易用性。我们优先考虑实际性能和
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此表显示了在 COCO 数据集上训练的各种 YOLOv5 模型的性能指标。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>验证集<br>50-95 | mAP<sup>验证集<br>50 | 速度<br><sup>CPU b1<br>(毫秒) | 速度<br><sup>V100 b1<br>(毫秒) | 速度<br><sup>V100 b32<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>验证集<br>50-95 | mAP<sup>验证集<br>50 | 速度<br><sup>CPU b1<br>(毫秒) | 速度<br><sup>V100 b1<br>(毫秒) | 速度<br><sup>V100 b32<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------- | ----------------------- | -------------------- | ----------------------------- | ------------------------------ | ------------------------------- | ---------------- | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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<details>
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<summary>表格说明</summary>
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@ -295,13 +292,13 @@ YOLOv5 [发布版本 v7.0](https://github.com/ultralytics/yolov5/releases/v7.0)
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YOLOv5 分割模型在 [COCO 数据集](https://docs.ultralytics.com/datasets/segment/coco/)上使用 A100 GPU 训练了 300 个周期，图像大小为 640 像素。模型被导出为 [ONNX](https://onnx.ai/) FP32 用于 CPU 速度测试，以及 [TensorRT](https://developer.nvidia.com/tensorrt) FP16 用于 GPU 速度测试。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 笔记本上进行，以确保可复现性。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>框<br>50-95 | mAP<sup>掩码<br>50-95 | 训练时间<br><sup>300 周期<br>A100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TRT A100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
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| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
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| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
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| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
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| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>框<br>50-95 | mAP<sup>掩码<br>50-95 | 训练时间<br><sup>300 周期<br>A100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TRT A100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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| ------------------------------------------------------------------------------------------ | ------------------- | ------------------- | --------------------- | ---------------------------------------- | ------------------------------- | ------------------------------- | ---------------- | ---------------------- |
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| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
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| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
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| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
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| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
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| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
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- 所有检查点均使用 SGD 优化器，`lr0=0.01` 和 `weight_decay=5e-5`，在图像大小为 640 像素下，使用默认设置训练了 300 个周期。<br>训练运行记录在 [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official)。
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- **准确度** 值表示在 COCO 数据集上的单模型、单尺度性能。<br>重现请使用：`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
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@ -352,7 +349,7 @@ model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
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```
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| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
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| :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: |
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### 导出
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@ -376,23 +373,23 @@ YOLOv5 [发布版本 v6.2](https://github.com/ultralytics/yolov5/releases/v6.2)
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YOLOv5-cls 分类模型在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 上使用 4xA100 实例训练了 90 个周期。[ResNet](https://arxiv.org/abs/1512.03385) 和 [EfficientNet](https://arxiv.org/abs/1905.11946) 模型在相同设置下进行了训练以供比较。模型被导出为 [ONNX](https://onnx.ai/) FP32（CPU 速度测试）和 [TensorRT](https://developer.nvidia.com/tensorrt) FP16（GPU 速度测试）。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 上运行以确保可复现性。
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| 模型 | 尺寸<br><sup>(像素) | 准确率<br><sup>top1 | 准确率<br><sup>top5 | 训练<br><sup>90 周期<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TensorRT V100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@224 (B) |
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| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
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| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
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| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
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| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
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| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
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| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
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| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
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| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
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| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
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| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
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| | | | | | | | | |
|
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| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
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| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
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| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
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| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
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| 模型 | 尺寸<br><sup>(像素) | 准确率<br><sup>top1 | 准确率<br><sup>top5 | 训练<br><sup>90 周期<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TensorRT V100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@224 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | ------------------- | ------------------- | ------------------- | ------------------------------------- | ------------------------------- | ------------------------------------ | ---------------- | ---------------------- |
|
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| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
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| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>表格说明（点击展开）</summary>
|
||||
|
|
|
|||
|
|
@ -261,8 +261,8 @@ python utils/loggers/comet/hpo.py \
|
|||
Execute multiple sweep trials concurrently using the `comet optimizer` command:
|
||||
|
||||
```shell
|
||||
comet optimizer -j <num_workers> \
|
||||
utils/loggers/comet/optimizer_config.json utils/loggers/comet/hpo.py
|
||||
comet optimizer -j \
|
||||
utils/loggers/comet/hpo.py < num_workers > utils/loggers/comet/optimizer_config.json
|
||||
```
|
||||
|
||||
Replace `<num_workers>` with the desired number of parallel processes.
|
||||
|
|
|
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Loading…
Reference in New Issue