Artificial Intelligence && Deep Learning
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Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:
Показати більше📈 Аналітичний огляд Telegram-каналу Artificial Intelligence && Deep Learning
Канал Artificial Intelligence && Deep Learning (@deeplearning_ai) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 57 527 підписників, посідаючи 2 234 місце в категорії Технології та додатки та 5 894 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 57 527 підписників.
За останніми даними від 03 вересня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -235, а за останні 24 години на 2, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 0%. Протягом перших 24 годин після публікації контент зазвичай збирає N/A% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 0 переглядів. Протягом першої доби публікація в середньому набирає 0 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 0.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як github, learning, estimation, dataset, engineer.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact:”
Завдяки високій частоті оновлень (останні дані отримано 04 вересня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
57 527
Підписники
+224 години
-537 днів
-23530 днів
Архів дописів
Repost from Machine Learning with Python
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🚀 Introducing L2CS-Net: Fine-Grained Gaze Estimation 👀✨
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#AI #DeepLearning #GazeEstimation #L2CSNet #OpenSource 🚀🚀 Introducing L2CS-Net: Fine-Grained Gaze Estimation 👀✨
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✅ Supports Gaze360 dataset
✅ Train with Single-GPU / Multi-GPU
✅ Demo for real-time visualization
📌 Quick Start:
🗂️ Prepare dataset
🏋️ Train (
python main.py --train)
🎥 Video Infernece (python main.py --demo)
🌟 Support Open Source! Star ⭐ & Share!
🔗 GitHub Repo: L2CSNet
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Pippo: High-Resolution Multi-View Humans from a Single Image
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🚀 3DGazeNet: Revolutionizing Gaze Estimation with Weak-Supervision! 🌟
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📖Read the Paper: Access Here
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Join us in pushing the boundaries of gaze estimation technology with 3DGazeNet!
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
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* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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⚽ FIFA World Cup 3D Human Pose Dataset ⚽
Discover WorldPose: a cutting-edge dataset for multi-person global pose estimation, featuring footage from the 2022 FIFA World Cup with 2.5M+ annotations!
Key Highlights:
✅ Authors: ETH, FIFA, Microsoft
✅ 150K+ frames, 2.5M+ 3D poses (SMPL format)
✅ Calibrated moving cameras & accurate trajectories
📄 Paper: arxiv.org/pdf/2501.02771
📊 Dataset: Access here
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🔥 Shanghai AI Lab Introduces RTMW: Real-Time Multi-Person Whole-Body Pose Estimation 🔥
• Blazing Speed: 940+ FPS on GPU, 50–300+ FPS on CPU
• High-Performance Models: Supports 2D/3D whole-body pose estimation
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• Open Source: Code and pre-trained models available
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
🔥 Shanghai AI Lab Introduces RTMW: Real-Time Multi-Person Whole-Body Pose Estimation 🔥
• Blazing Speed: 940+ FPS on GPU, 50–300+ FPS on CPU
• High-Performance Models: Supports 2D/3D whole-body pose estimation
• Cutting-Edge Architecture: Built upon RTMPose with FPN & HEM
• Wide Dataset Coverage: Manually aligned keypoints across 14 open datasets
• Extensive Framework Support: ONNX, TensorRT, ncnn, and OpenVINO
• Open Source: Code and pre-trained models available
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
🔗 Discover More:
* Source code: Github
* Paper: Read the paper
✅ https://t.me/deeplearning_ai
