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Deep Learning Project.\n\n## 🎯 Goal\nRecognize hand gestures using state-of-the-art neural networks to control smart TVs without a remote.\n\n## 🔍 Problem Statement\n\nAs a data scientist at a home electronics company, your mission is to develop an innovative feature for smart TVs that recognizes five distinct hand gestures, enabling users to control their TV without a remote.\n\n### 🖐️ Gestures and Commands:\n- 👍 Thumbs up: Increase volume\n- 👎 Thumbs down: Decrease volume\n- 👈 Left swipe: Jump backwards 10 seconds\n- 👉 Right swipe: Jump forward 10 seconds\n- ✋ Stop: Pause the movie\n\nEach gesture is captured as a sequence of 30 frames by a webcam mounted on the TV.\n\n## 📊 Dataset\n\n- Training data: Hundreds of categorized videos\n- Video length: 2-3 seconds\n- Frame sequence: 30 frames per video\n- Recorded by: Various individuals performing gestures\n- Data structure: 'train' and 'val' folders with corresponding CSV files\n- Video dimensions: 360x360 or 120x160\n\n[Download Dataset](https://drive.google.com/uc?id=1ehyrYBQ5rbQQe6yL4XbLWe3FMvuVUGiL)\n\n## 🚀 Challenge\n\nTrain a model on the 'train' folder that performs well on the 'val' folder.\n\n## 🛠️ Technologies Used\n\n- Python\n- TensorFlow / PyTorch\n- OpenCV\n- Numpy\n- Pandas\n\n## 🚀 Getting Started\n\n1. Clone this repository\n2. Download and extract the dataset\n3. Install required dependencies\n4. Run the Jupyter notebook or Python scripts\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavsharma07%2Fhand_gesture_recognition-deep_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhinavsharma07%2Fhand_gesture_recognition-deep_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhinavsharma07%2Fhand_gesture_recognition-deep_learning/lists"}