updates

.github/workflows/greetings.yml

@@ -14,7 +14,7 @@ jobs:
           Hello @${{ github.actor }}, thank you for your interest in our work! Please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) to get started, and see our [Google Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb), [Docker Image](https://hub.docker.com/r/ultralytics/yolov5), and [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) for example environments.
             
           If this is a bug report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
-
+            
           If this is a custom model or data training question, please note that Ultralytics does **not** provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
           - **Cloud-based AI** surveillance systems operating on **hundreds of HD video streams in realtime.**
           - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**

utils/utils.py

@@ -993,13 +993,14 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_st
 
         j = y[3].argmax() + 1
         ax[7].plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, label=Path(f).stem)
-        ax[7].plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.5, 39.1, 42.5, 45.9, 49., 50.5],
-                   '.-', linewidth=2, markersize=8, label='EfficientDet')
-        ax[7].set_xlabel('Latency (ms)')
-        ax[7].set_ylabel('COCO AP val')
 
-    ax[7].legend()
+    ax[7].plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.5, 39.1, 42.5, 45.9, 49., 50.5],
+               '.-', linewidth=2, markersize=8, label='EfficientDet')
     ax[7].set_xlim(0)
+    ax[7].set_xlabel('Latency (ms)')
+    ax[7].set_ylabel('COCO AP val')
+    ax[7].legend()
+
     plt.savefig(f.replace('.txt', '.png'), dpi=200)