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.
👉🏻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/
👉🏻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