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README.md

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-license: mit
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+# 🎮 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`)
+
+---
+