title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license	short_description	header
YOLOX CPU	🍺	green	gray	gradio	4.37.2	app.py	false	creativeml-openrail-m	Ultralytics | YOLO v8	mini

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Space Link : https://huggingface.co/spaces/prithivMLmods/YOLOX-CPU

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/spaces/prithivMLmods/YOLOX-CPU

# If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/prithivMLmods/YOLOX-CPU

🚨 New Release: Ultralytics 8.2.51 🍺Live Space for Demo : prithivMLmods/YOLOX-CPU, Duplicate the Space to avoid queuing issues.

Sample Demo

👉🏻For HPC, use A100/T4 under controlled conditions. 👉🏻Speed Estimation, Object Counting, Distance Calculation, Workout Monitoring, Heatmaps

Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 🔥, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

🔗 https://pypi.org/project/ultralytics/8.2.51/

🚀More Features You can try:
✅ Classes selection support added
✅ Live FPS display in the sidebar
✅ Webcam and video support added
✅ Confidence and NMS threshold option to modify.
✅ Segmentation, detection, and pose models support added.

🙀Ultralytics Live inference: https://docs.ultralytics.com/guides/streamlit-live-inference/

from ultralytics import solutions
solutions.inference()
### Make sure to run the file using command `streamlit run <file-name.py>`

⚡yolo streamlit-predict

👉🏻Advantages of Live Inference

☑️ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
☑️Efficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources.

🙀Ultralytics feature Models: https://docs.ultralytics.com/models/, Ultralytics new Solutions: https://docs.ultralytics.com/solutions/

Demo Screenshot 1

Demo Screenshot 2

Working Demo Gif 3

👉🏻Official Documentation: Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. 🔗 https://docs.ultralytics.com/ .

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.@prithivmlmods