MAS-AI-0000/GameNet-1

🎮 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).

🚀 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

📁 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)

🏗️ 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)

📈 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)

🧪 Inference

Try It Out

GameNET-1 API Endpoint:

DOCS: