| --- |
| license: mit |
| datasets: |
| - FronkonGames/steam-games-dataset |
| metrics: |
| - accuracy |
| base_model: |
| - google/efficientnet-b3 |
| pipeline_tag: image-classification |
| tags: |
| - game |
| --- |
| # 🎮 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](https://www.kaggle.com/datasets/fronkongames/steam-games-dataset) |
| - 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: |
| ](https://mas-ai-0000-gamenet-1.hf.space/predict) |
|
|
| [DOCS: |
| ](https://mas-ai-0000-gamenet-1.hf.space/docs) |
