From 121f4ba20532d0a8d773d7cc36093a1f1f8564c1 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Tue, 22 Feb 2022 12:35:24 +0100
Subject: [PATCH] YOLOv5 v6.1 release (#6739)

---
 README.md | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 7bfea7c24e8f..b03a7c583c40 100644
--- a/README.md
+++ b/README.md
@@ -215,11 +215,11 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 
 ## <div align="center">Why YOLOv5</div>
 
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
 <details>
   <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
 
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
 </details>
 <details>
   <summary>Figure Notes (click to expand)</summary>
@@ -238,22 +238,22 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 
 |Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |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)
 |---                    |---  |---    |---    |---    |---    |---    |---    |---
-|[YOLOv5n][assets]      |640  |28.4   |46.0   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
-|[YOLOv5s][assets]      |640  |37.2   |56.0   |98     |6.4    |0.9    |7.2    |16.5
-|[YOLOv5m][assets]      |640  |45.2   |63.9   |224    |8.2    |1.7    |21.2   |49.0
-|[YOLOv5l][assets]      |640  |48.8   |67.2   |430    |10.1   |2.7    |46.5   |109.1
+|[YOLOv5n][assets]      |640  |28.0   |45.7   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
+|[YOLOv5s][assets]      |640  |37.4   |56.8   |98     |6.4    |0.9    |7.2    |16.5
+|[YOLOv5m][assets]      |640  |45.4   |64.1   |224    |8.2    |1.7    |21.2   |49.0
+|[YOLOv5l][assets]      |640  |49.0   |67.3   |430    |10.1   |2.7    |46.5   |109.1
 |[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
 |                       |     |       |       |       |       |       |       |
-|[YOLOv5n6][assets]     |1280 |34.0   |50.7   |153    |8.1    |2.1    |3.2    |4.6
-|[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |12.6   |16.8
-|[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
-|[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.7   |111.4
-|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
+|[YOLOv5n6][assets]     |1280 |36.0   |54.4   |153    |8.1    |2.1    |3.2    |4.6
+|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |16.8   |12.6
+|[YOLOv5m6][assets]     |1280 |51.3   |69.3   |887    |11.1   |6.8    |35.7   |50.0
+|[YOLOv5l6][assets]     |1280 |53.7   |71.3   |1784   |15.8   |10.5   |76.8   |111.4
+|[YOLOv5x6][assets]<br>+ [TTA][TTA]|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>- 
 
 <details>
   <summary>Table Notes (click to expand)</summary>
 
-* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
+* All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
 * **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
 * **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
 * **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`