if:(github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
if:(github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
diff = response.text if response.status_code == 200 else f"Failed to get diff: {response.content}"
diff = response.text if response.status_code == 200 else f"Failed to get diff: {response.content}"
# Set up client
# Get summary
client = openai.AzureOpenAI(
api_key=OPENAI_AZURE_API_KEY,
api_version=OPENAI_AZURE_API_VERSION,
azure_endpoint=OPENAI_AZURE_ENDPOINT
)
messages = [
messages = [
{
{
"role": "system",
"role": "system",
"content": "You are an Ultralytics AI assistant skilled in software development and technical communication. Your task is to summarize GitHub releases from Ultralytics in a way that is detailed, accurate, and understandable to both expert developers and non-expert users. Focus on highlighting the key changes and their impact in simple and intuitive terms."
"content": "You are an Ultralytics AI assistant skilled in software development and technical communication. Your task is to summarize GitHub releases in a way that is detailed, accurate, and understandable to both expert developers and non-expert users. Focus on highlighting the key changes and their impact in simple and intuitive terms."
},
},
{
{
"role": "user",
"role": "user",
"content": f"Summarize the updates made in the [Ultralytics](https://ultralytics.com) '{latest_tag}' tag, focusing on major changes, their purpose, and potential impact. Keep the summary clear and suitable for a broad audience. Add emojis to enliven the summary. Reply directly with a summary along these example guidelines, though feel free to adjust as appropriate:\n\n"
"content": f"Summarize the updates made in the '{latest_tag}' tag, focusing on major changes, their purpose, and potential impact. Keep the summary clear and suitable for a broad audience. Add emojis to enliven the summary. Reply directly with a summary along these example guidelines, though feel free to adjust as appropriate:\n\n"
f"## 🌟 Summary (single-line synopsis)\n"
f"## 🌟 Summary (single-line synopsis)\n"
f"## 📊 Key Changes (bullet points highlighting any major changes)\n"
f"## 📊 Key Changes (bullet points highlighting any major changes)\n"
f"## 🎯 Purpose & Impact (bullet points explaining any benefits and potential impact to users)\n"
f"## 🎯 Purpose & Impact (bullet points explaining any benefits and potential impact to users)\n"
f"\n\nHere's the release diff:\n\n{diff[:300000]}",
f"\n\nHere's the release diff:\n\n{diff[:300000]}",
The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.
The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.
<strong>Watch:</strong> How to Train Ultralytics YOLO Models on the VisDrone Dataset for Drone Image Analysis
</p>
VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
Francesco Mattioli
3 months ago
committed by
GitHub