We dont need to add models

.gitignore

@@ -4,4 +4,5 @@
 venv/
 /datasets/annotations/*
 /datasets/images/*
-src/__pycache__/
+src/__pycache__/
+models/lego

README.md

@@ -11,3 +11,118 @@ short_description: Using RCNN and Fully connected to detect Planes in objects
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+
+
+# LEGO Object Detection using Faster R-CNN
+
+![Faster R-CNN](https://upload.wikimedia.org/wikipedia/commons/4/4b/Faster_R-CNN.png)
+
+This project trains a **Faster R-CNN** model with a **ResNet-50 backbone** to detect LEGO objects using a custom dataset.
+
+---
+
+## **Project Structure**
+```yaml
+lego_detection/
+│── models/                   # Trained models
+│   ├── lego_fasterrcnn.pth   # Saved model
+│
+│── datasets/                  # Dataset folder
+│   ├── images/                # Training images
+│   ├── annotations/           # Corresponding XML annotations
+│
+│── src/                       # Source code
+│   ├── dataset.py             # Dataset class (LegoDataset)
+│   ├── train.py               # Training script
+│   ├── evaluate.py            # mAP Calculation
+│   ├── utils.py               # IoU, AP calculation functions
+│
+│── config.yaml                # Hyperparameters & settings
+│── README.md                  # Project documentation
+```
+
+---
+
+## ⚡ **Setup Instructions**
+### **1️⃣ Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+### **2️⃣ Update Configuration**
+Modify **`config.yaml`** to adjust **hyperparameters, dataset paths, and model settings**.
+
+---
+
+## 🚀 **Training the Model**
+Run the following command to start training:
+```bash
+python src/train.py
+```
+This script will:
+✅ Train Faster R-CNN with **LegoDataset**  
+✅ Log training **loss & mAP**  
+✅ Save the trained model in `models/lego_fasterrcnn.pth`
+
+---
+
+## 📊 **Monitoring Training Progress**
+Use the Jupyter Notebook to **visualize loss & mAP over epochs**:
+```bash
+jupyter notebook notebooks/training_visualization.ipynb
+```
+
+---
+
+## 🛠️ **Hyperparameters (`config.yaml`)**
+Modify the **`config.yaml`** file to fine-tune the model:
+```yaml
+model:
+  backbone: resnet50
+  num_classes: 2
+  pretrained: true
+  learning_rate: 0.0001
+  epochs: 5
+  batch_size: 8
+  optimizer: adam
+
+dataset:
+  image_dir: datasets/images
+  annotation_dir: datasets/annotations
+  train_split: 0.8
+  val_split: 0.2
+
+evaluation:
+  iou_threshold: 0.5
+```
+
+---
+
+## 📡 **Evaluating the Model**
+Once training is complete, evaluate performance using:
+```bash
+python src/evaluate.py
+```
+
+---
+
+## 💡 **Troubleshooting & Tips**
+### ❓ **Training Takes Too Long?**
+- Reduce `epochs` in `config.yaml`
+- Use a **smaller dataset** for testing
+
+### ❓ **mAP is too low?**
+- Increase `epochs`
+- Check dataset annotations
+- Tune learning rate
+
+---
+
+## 🏆 **Contributors**
+- 👤 **Alex** - Machine Learning Engineer
+
+📧 **Contact**: [Your Email]  
+
+🚀 **Happy Training!**