🎮 GameNet-1

GameNet-1 is a deep learning-based computer vision system designed to recognize video games based on their cover art or in-game screenshots. Built using EfficientNet and trained on a curated dataset of popular Steam games, the model predicts both the game name and its genre(s).

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🚀 Features

• 🔍 Recognizes games from screenshots or cover images
• 🧠 Powered by EfficientNetB3 for high accuracy
• 🗂️ Trained only on popular games with over 2M estimated owners
• 🎯 Fine-tuned and augmented for better generalization
• 📊 Shows prediction confidence alongside game metadata

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📁 Dataset

• Source: Steam Games Dataset on Kaggle
• Filtered for popular games with over 2 million estimated owners
• Images:
  • Header cover image
  • 5 in-game screenshots (JPEG only)

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🏗️ Model Architecture

• Base: EfficientNetB3 pretrained on ImageNet
• Input Size: 300x300 RGB
• Top Layers:
  • GlobalAveragePooling2D
  • Dropout (0.4 & 0.2)
  • Dense(256, relu)
  • Dense(n_classes, softmax)
• Training:
  • Phase 1: Frozen base
  • Phase 2: Fine-tuned base (lower LR)

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📈 Performance

• Accuracy (val set): 30%
• Trained using:
  • categorical_crossentropy loss
  • Adam optimizer (1e-3 for frozen, 1e-5 for fine-tune)
  • Real-time data augmentation (ImageDataGenerator)

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🧪 Inference

Try It Out

GameNET-1 API Endpoint:

DOCS: