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README.md

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-[README.md](https://github.com/user-attachments/files/21972618/README.md)
-# AgStream
-# Inference Optimization Project
+# AgStream - Plant Classification Pipeline For NVIDIA Edge Devices
 
-Welcome! 🚀  
-We’re really happy to have you here. This project is your chance to explore how cutting-edge AI models can be made **faster, smarter, and lighter** — the same kind of work that powers self-driving cars, mobile AI apps, and real-time decision systems.
+![Plant Classification](docs/images/agstream-hero-image.png)
 
-Think of it like tuning a race car: the model already drives, but now we’ll make it smooth, efficient, and lightning-fast. Along the way you’ll get to experiment, break things, fix them, and share what you’ve learned. Collaboration is key — at the beginning, you’ll be working in pairs, supporting each other, and reviewing each other’s work, just like in real engineering teams.
+## 🌾 Product Description
+AgStream is a **real-time inference pipeline** for crop and weed classification, optimized for NVIDIA Jetson devices. The system demonstrates end-to-end model optimization from PyTorch → ONNX → TensorRT deployment with **DeepStream 6.4**.
 
-Here’s a real-world connection: the model we’re starting from is accurate, but not yet fast enough for real-time use in practical applications. Engineers often face this exact challenge - they have a working solution, but it’s too slow to be useful in the field. By applying optimizations, exporting to ONNX, and leveraging TensorRT, you’ll experience first-hand how these research models become deployable systems that can actually run on devices like laptops or even Jetsons.
+**Key Capabilities:**
+- **Real-time Processing:** Classify RTSP video streams with metadata extraction
+- **Edge Deployment:** Optimized for NVIDIA Jetson platforms
+- **Model Optimization:** PyTorch → ONNX → TensorRT conversion
+- **Agricultural Focus:** 83 crop and weed categories from CropAndWeed dataset
 
-Most importantly: don’t stress. This is a safe place to try, fail, and learn. Mistakes are part of the journey, and every one of them makes you stronger.
+---
 
-## Next Steps
-- Check out [our collaboration guide](docs/general/collaboration_guide.md)!
-- Check out [setup_guide.md](docs/general/setup_guide.md) if you need help running things.
-- See the weekly goals in [milestones.md](docs/general/milestones_and_weekly_breakdown.md).
-- Ask questions, share insights, and help your teammates — you’re not alone in this!
+## 🔧 Hardware & Software Requirements
 
-Good luck, and let’s make this model fly 🚀
+### Target Platform
+- **Device:** NVIDIA Jetson Orin Nano (Developer Kit)
+- **JetPack:** 6.2 [L4T 36.4.3]
+- **DeepStream SDK:** 6.4 (Triton multi-arch)
+- **CUDA:** 12.6
+- **TensorRT:** 10.3
+- **Memory:** 8GB RAM
+- **OpenCV:** 4.8.0 (GPU support depends on build)
+---
 
-## AgStream - Implement Basic PyTorch Inference Script
+## 📊 Performance Metrics
 
-This branch includes the implementation of a PyTorch inference script and related updates.
+Evaluation was done using the PyTorch model, with inputs resized to 256×256 (CPU inference).
 
-### Changes Made:
-1. **Inference Script**:
-   - Supports MobileNet and ResNet models.
-   - Processes images and saves predictions to `outputs/predictions.json`.
+### Classification Accuracy (CropAndWeed Dataset)
+| Model | 83-Class | Binary (Crop/Weed) | 9-Class | 24-Class | Model Size |
+|-------|----------|-------------------|---------|----------|------------|
+| MobileNet | 67.2% | 85.2% | 84.6% | 43.6% | 28.3 MB |
+| ResNet18 | 67.2% | 82.1% | 81.5% | 41.0% | 135 MB |
 
-2. **Dataset Documentation**:
-   - Added `docs/data.md` with details about the CropAndWeed dataset.
+### Inference Latency (CPU)
+| Model | Average Latency | Std Dev |
+|-------|----------------|---------|
+| MobileNet | 55.1ms | 26.7ms |
+| ResNet18 | 84.9ms | 51.4ms |
 
-3. **Model Documentation**:
-   - Added `docs/models.md` with performance metrics for MobileNet and ResNet.
+> ⚠ Accuracy improves with hierarchical classification (fewer classes)
 
-4. **Test Images**:
-   - Added sample images in `assets/test_images/`.
+---
 
-5. **Pretrained Models**:
-   - Added pretrained MobileNet and ResNet models in `data/models/`.
+## 🚀 Quick Start
 
-6. **Environment Setup**:
-   - Added `env/Dockerfile` and `env/requirements.txt`.
+### 1. Environment Setup
 
-### How to Run:
-1. Install dependencies:
-   ```bash
-   pip install -r env/requirements.txt
-   ```
+```bash
+bash scripts/run_dev_jetson.sh
+```
 
-2. Run inference:
-   ```bash
-   python inference.py
-   ```
+### 2. Run Pipeline
+
+```bash
+# Start RTSP server (terminal 1)
+python src/rtsp/rtsp_server.py
+
+# Run classification pipeline (terminal 2)
+python src/deepstream/pipelines/deepstream_pipeline_cpu.py
+# or
+python src/deepstream/pipelines/deepstream_pipeline_gpu.py
+
+# Optional: run metadata extraction
+python src/deepstream/pipelines/access_metadata.py
+```
+
+---
+
+## 🧠 Research and Development
+
+### 1. Model Conversion & Optimization
+
+```bash
+# Export PyTorch to ONNX
+python scripts/export_to_onnx.py resnet18
+python scripts/export_to_onnx.py mobilenet
+# TensorRT engine generation is automatic
+```
+
+### 2. Performance Benchmarking
+
+```bash
+python src/deepstream/speed_benchmark.py
+```
+
+---
+
+## 🎯 Pipeline Architecture
+**RTSP Stream → H.264 Decode → Video Convert → Stream Mux → AI Inference (TensorRT) → OSD Overlay → JPEG Encode → Frame Output**
+
+![pipeline schema](docs/images/pipeline-schema.png)
+
+**Processing Details:**
+- Input: 256×256 RGB frames from RTSP
+- Normalization: mean=[0.5,0.5,0.5], std=[0.25,0.25,0.25]
+- Batch Size: 1 (real-time)
+- Precision: FP16 (default; configurable)
+---
+
+## 📁 Project Structure
+* `src/` – Pipeline logic, inference modules, conversion scripts, evaluation
+* `models/` – Trained models (PyTorch, ONNX, TensorRT)
+* `scripts/` – Execution and export scripts
+* `env/` – Environment setup per target (Jetson / CPU)
+* `configs/` – Configuration files for pipeline and models
+* `assets/` – Test data (images, videos)
+* `docs/` – Documentation
+
+---
+
+## 🔬 Technical Details
+- Dataset: CropAndWeed (WACV 2023), 83 categories
+- Training: PyTorch
+- Export: PyTorch → ONNX (opset 17)
+- Optimization: ONNX → TensorRT
+- Deployment: DeepStream Python API
+- Container: nvcr.io/nvidia/deepstream-l4t:6.4-triton-multiarch, Python 3.10, OpenCV 4.11 CUDA
+
+**Development Focus:**
+- Model optimization & performance analysis
+- Edge deployment & real-time inference
+- End-to-end video processing & metadata extraction
+- Benchmarking: latency & throughput
+
+**Code Quality:**
+
+```bash
+isort src/ && black src/ && flake8 src/
+```
+
+**Model Evaluation:**
+
+```bash
+python src/evaluation/run_evaluation.py
+python src/evaluation/run_hierarchical_evaluation.py
+```
+
+---
+
+⭐ If you found this project useful, consider giving it a star
 
-3. View results:
-   - Predictions are saved in `outputs/predictions.json`.