Readme.md

Bird-Drone Classifier
The Bird-Drone Classifier is a machine learning model built using the Hugging Face ecosystem to accurately distinguish between birds and drones in images. It is optimized for airspace monitoring, security systems, and wildlife observation applications.

Table of Contents
Overview
Features
Hugging Face Model
Installation
Usage
Dataset
Training and Fine-tuning
Technologies
Future Work
Contributing
License
Overview
The Bird-Drone Classifier uses advanced machine learning algorithms to classify objects in images as either birds or drones. The model is hosted on the Hugging Face Model Hub for easy access and deployment.

Use Cases
Prevent unauthorized drone activity in restricted airspaces.
Enhance wildlife monitoring systems.
Reduce false positives in drone detection systems.
Features
Pre-trained Model: Available on the Hugging Face Model Hub for immediate use.
Custom Fine-tuning: Supports dataset customization for specific use cases.
Real-time Integration: Can be used with live video feeds or batch image processing.
Hugging Face Model
The pre-trained model is available on the Hugging Face Model Hub:
👉 Bird-Drone Classifier on Hugging Face

Quick Start
python
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from transformers import AutoModelForImageClassification, AutoProcessor
from PIL import Image

# Load the model and processor
model = AutoModelForImageClassification.from_pretrained("your-username/bird-drone-classifier")
processor = AutoProcessor.from_pretrained("your-username/bird-drone-classifier")

# Load and process the image
image = Image.open("input_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Predict
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

classes = ["Bird", "Drone"]
print(f"Predicted Class: {classes[predicted_class]}")
Installation
Clone the repository:

bash
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git clone https://github.com/yourusername/bird-drone-classifier.git
cd bird-drone-classifier
Install dependencies:

bash
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pip install -r requirements.txt
Install Hugging Face libraries:

bash
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pip install transformers datasets evaluate huggingface_hub
Usage
Image Classification
Run the classification script:

bash
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python classify.py --image_path ./input_image.jpg
Real-Time Classification
To process a video feed:

bash
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python real_time_detect.py --video_path ./sample_video.mp4
Dataset
The dataset includes:

Bird Images: Sourced from wildlife image datasets like iNaturalist.
Drone Images: Collected from publicly available datasets.
Dataset Structure
bash
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dataset/
├── train/
│ ├── birds/
│ ├── drones/
├── test/
│ ├── birds/
│ ├── drones/
Loading Dataset with Hugging Face Datasets
python
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from datasets import load_dataset

dataset = load_dataset("your-dataset-name/bird-drone-dataset")
Training and Fine-tuning
Train or fine-tune the model using the Hugging Face Trainer API:

bash
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python train.py --dataset_path ./dataset --epochs 10 --output_dir ./model_output
You can also push your fine-tuned model to the Hugging Face Model Hub:

python
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from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
output_dir="your-hub-repo-name",
evaluation_strategy="steps",
save_steps=500,
push_to_hub=True
)

trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"]
)

trainer.train()
trainer.push_to_hub()
Technologies
Hugging Face Transformers: Model and training tools.
Hugging Face Datasets: Dataset management.
Python: Core programming language.
PyTorch: Model backbone.
Future Work
Add support for more diverse datasets.
Deploy the model as a Hugging Face Space for web-based interaction.
Improve detection accuracy with additional sensor data (e.g., audio, thermal).
Contributing
We welcome contributions! Please follow these steps:

Fork the repository.
Create a new branch (git checkout -b feature-name).
Commit your changes (git commit -m "Add new feature").
Push to the branch (git push origin feature-name).
Open a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.

README.md

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