This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣  Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/codeprogrammer

📌 Project name: Cat/Dog/Fox Lightning 2024

📝 Language: #Python

🔗 Dataset Link: https://www.kaggle.com/datasets/snmahsa/animal-image-dataset-cats-dogs-and-foxes

🖋 Download the dataset offline:
https://t.iss.one/datasets1/668

📌 Project name: Cat/Dog/Fox Lightning 2024

By: https://t.iss.one/DataScienceN 🌟

🔖 Project name : Analysis of Covid-19 chest x-rays

📝 In order to diagnose patients with Covid-19, the analysis of chest X-rays is a possibility to be explored to more easily detect positive cases. If the classification through deep learning of such data proves effective in detecting positive cases, then this method can be used in hospitals and clinics when traditional testing cannot be done.

📌 Repo:
https://github.com/rehabaam/ds_covid19_project

🔍 By: https://t.iss.one/DataScienceN 🌟

🔖 ImageBind: One Embedding Space To Bind Them All

📝 This project is a significant step forward in understanding and connecting information from diverse sources like images, text, audio, video, and even motion sensor data.

⚙️ Supports 6 Modalities:

📷 Image
📝 Text
🔈 Audi
🎥 Video
🦴 IMU sensor data (e.g., accelerometer)
🙄 Depth/Thermal & 3D data
Interestingly, only some modalities had labels, yet ImageBind learned to align them through self-supervised learning.


💫 Key Features:

..No need for paired data (e.g., images and audio don’t have to be aligned)..Leverages contrastive learning for learning joint embedding space
..Competes with CLIP and AudioCLIP, but with better accuracy and coverage..Enables zero-shot retrieval (e.g., finding relevant video using just a sentence)


📌 Repo: https://github.com/facebookresearch/ImageBind

🔍 By: https://t.iss.one/DataScienceN 🌟

#ImageBind #MultimodalAI #MetaAI #DeepLearning #SelfSupervised

😍😍😍😍😍😍😍😍
It’s truly fascinating — definitely worth diving deeper into and working on!
😍😍😍😍😍😍😍😍

Cupcake Counting Project on the Production Line Using Ultralytics YOLO 🧁

🚀 With the rapid growth of the computer vision market in the bakery industry—projected to reach $23.42 billion by 2025—the practical applications of this technology are receiving increasing attention. One of the most important and common applications is the automated counting of bakery products on production lines.

In this project, the development team provided a model for cupcake detection, and Ultralytics solutions were used to implement the counting process. The only necessary step for deployment was updating the region coordinates for detection, which was successfully accomplished.

Advantages:
✅ Instantly detects and counts cupcakes as they move.
✅ Handles high-speed conveyor belt production effortlessly.

🔗 Complete code ➡️https://lnkd.in/d-4Zk2Q5

🔍 By: https://t.iss.one/DataScienceN

Ready for the most powerful foundation model for medical images/videos?

🚨 Just dropped: MedSAM2
The next-gen foundation model for 3D medical image & video segmentation — built on top of SAM 2.1.

Why it matters:
• Trained on 455K+ 3D image–mask pairs & 76K+ annotated video frames
• >85% reduction in human annotation costs (validated in 3 studies)
• Fast, accurate, and generalizes across organs, modalities, and pathologies

Big impact:
We used MedSAM2 to create 3 massive datasets:
• 5,000 CT lesions
• 3,984 liver MRI lesions
• 251,550 echo video frames

Plug & play:
Deployable in:
→ 3D Slicer
→ JupyterLab
→ Gradio
→ Google Colab

🔖 Project site: https://medsam2.github.io/

🔗 Paper: https://lnkd.in/gbXu6D64

🔍 By: https://t.iss.one/DataScienceN