🎮 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:
EfficientNetB3pretrained on ImageNet - Input Size: 300x300 RGB
- Top Layers:
GlobalAveragePooling2DDropout(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_crossentropylossAdamoptimizer (1e-3 for frozen, 1e-5 for fine-tune)- Real-time data augmentation (
ImageDataGenerator)
🧪 Inference
Try It Out
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Model tree for MAS-AI-0000/GameNet-1
Base model
google/efficientnet-b3
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://MAS-AI-0000/GameNet-1")