🧠 Inference using Microsoft Florence-2 with the Ultralytics Python Package 😍

✅ Object Detection:
The model performs exceptionally well in detecting various objects and demonstrates impressive zero-shot capabilities. This means it can identify objects without needing specific training on a particular dataset.
🔹 Use case: It is highly suitable for auto-annotating datasets in object detection format.

✅ Accuracy:
The model performs well in terms of accuracy,
but 🔺 it requires significant processing time, making it unsuitable for real-time applications.

✅ "DENSE_REGION_CAPTION" Feature:
This feature generates rich textual descriptions for different regions of the image.
In the video, it introduced excessive glittery effects.
📌 It’s better suited for single-frame usage rather than processing a sequence of video frames.

✅ "REFERRING_EXPRESSION_SEGMENTATION" Feature:
This feature segments areas of the image using expressions referring to them.
However, ⏱️ it is time-consuming, and in terms of accuracy and efficiency, the SAM (Segment Anything Model) performs slightly better than Florence-2.

📓 Notebook:
🔗 https://github.com/ultralytics/notebooks/blob/main/notebooks/how-to-use-florence-2-for-object-detection-image-captioning-ocr-and-segmentation.ipynb

🔍 By: https://t.iss.one/DataScienceN5

🚀 New Tutorial: Automatic Number Plate Recognition (ANPR) with YOLOv11 + GPT-4o-mini!


This hands-on tutorial shows you how to combine the real-time detection power of YOLOv11 with the language understanding of GPT-4o-mini to build a smart, high-accuracy ANPR system! From setup to smart prompt engineering, everything is covered step-by-step. 🚗💡

🎯 Key Highlights:
✅ YOLOv11 + GPT-4o-mini = High-precision number plate recognition
✅ Real-time video processing in Google Colab
✅ Smart prompt engineering for enhanced OCR performance

📢 A must-watch if you're into computer vision, deep learning, or OpenAI integrations!


🔗 Colab Notebook
▶️ Watch on YouTube


#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition

🔍 By : https://t.iss.one/DataScienceN

🔷 Ultralytics YOLO11!🚀

Developed by Jing Qiu and Glenn Jocher, YOLO11 represents a major leap forward in object detection technology, reflecting months of dedicated research and development by the Ultralytics team.


✅ YOLO11 Key Features:
- Enhanced architecture for high-precision detection and complex vision tasks
- Faster inference speeds with balanced accuracy
- Higher precision while using 22% fewer parameters
- Seamlessly deployable across edge devices, cloud, and GPU systems
- Full support for:
🔹 Object Detection
🔹 Segmentation
🔹 Classification
🔹 Pose Estimation
🔹 Oriented Bounding Boxes (OBB)

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⚡ Quick Start
Run inference instantly with:
yolo predict model="yolo11n.pt"

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📎 Learn more and explore the documentation here:
🔗 https://ow.ly/mKOC50Tyyok


🔍 By : https://t.iss.one/DataScienceN

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🚦 Traffic Lights Detection using Ultralytics YOLO11! 🧠🤖

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📡 By: https://t.iss.one/DataScienceN

Don't forget to attend this session!

🔥 SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation, has been accepted at hashtag#CVPR2025! 🎉

make #SegmentAnything wiser by enabling it to understand text prompts—all with just 4.9M additional trainable parameters.

🚀💡 What makes SAMWISE special?
🔹 Textual & Temporal Adapter for #SAM2 – We introduce a novel adapter that enables early fusion of text and visual features, allowing SAM2 to understand textual queries while modeling temporal evolution across frames.
🔹 Tracking Bias Correction – SAM2 tends to keep tracking an object even when a better match for the text query appears. Our learnable correction mechanism dynamically adjusts its focus, ensuring it tracks the most relevant object at every moment.

✨ State-of-the-art performance across multiple benchmarks:

✅ New SOTA on Referring Video Object Segmentation (RVOS)
✅ New SOTA on image-level Referring Segmentation (RIS)✅ Runs online
✅ Requires no fine-tuning of SAM2 weights
🚀 SAMWISE is the first text-driven segmentation approach built on SAM2 that achieves SOTA while staying lightweight and online.
🏠 Project page: https://lnkd.in/dtBHBVbG
💻 Code and models: https://lnkd.in/d-fadFGd
🔗 Paper: arxiv.org/abs/2411.17646

📡 By: https://t.iss.one/DataScienceN

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