https://github.com/anis196/bitesense
This project is a deep learning-based classification model using ResNet50 and TensorFlow to classify snake bites as Poisonous or Non-Poisonous based on wound patterns. The model is trained on an image dataset and fine-tuned for better accuracy using GPU.
https://github.com/anis196/bitesense
cuda-toolkit cudnn deep-neural-networks python resnet-50 tensorflow-gpu
Last synced: 8 months ago
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This project is a deep learning-based classification model using ResNet50 and TensorFlow to classify snake bites as Poisonous or Non-Poisonous based on wound patterns. The model is trained on an image dataset and fine-tuned for better accuracy using GPU.
- Host: GitHub
- URL: https://github.com/anis196/bitesense
- Owner: Anis196
- License: mit
- Created: 2025-02-12T17:13:56.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-23T15:14:54.000Z (8 months ago)
- Last Synced: 2025-06-23T16:33:20.249Z (8 months ago)
- Topics: cuda-toolkit, cudnn, deep-neural-networks, python, resnet-50, tensorflow-gpu
- Language: Python
- Homepage:
- Size: 26.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🐍BiteSense : Snake Bite Classification using GPU acceleration
This project is a **deep learning-based classification model** using **ResNet50** and **TensorFlow** to classify **snake bites as Poisonous or Non-Poisonous** based on wound patterns. The model is trained on an image dataset and fine-tuned for better accuracy using GPU.
---
## 🚀 Features
✅ **Deep Learning Model**: Uses ResNet50 as a feature extractor.
✅ **Transfer Learning**: Fine-tuned for improved accuracy.
✅ **Data Augmentation**: Helps generalization on unseen data.
✅ **Real-time Predictions**: Classifies images of snake bites.
✅ **GPU Acceleration**: Supports CUDA for faster training.
---
## 🛠️ Installation & Setup
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/anis196/snk-bite-det.git
cd snk-bite-det
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Check GPU Availability (Optional)
```python
import tensorflow as tf
print("Num GPUs Available:", len(tf.config.list_physical_devices('GPU')))
```
### 4️⃣ Prepare the Dataset
Organize the dataset in the following structure:
```plaintext
/dataset
/Poisonous
- image1.jpg
- image2.jpg
/Non_Poisonous
- image1.jpg
- image2.jpg
```
🔹 **Update the dataset path** in `snk.py` before running the script.
---
## 🎯 Model Training & Usage
### 5️⃣ Train or Load the Model
If running for the first time, the model will train and save automatically.
```bash
python snk.py
```
To avoid retraining, the model is saved as `resnet50_snake_bite_classifier.h5` and will be loaded in future runs.
---
## 🐍 Making Predictions
Use an image file to test the model:
```python
from snk import predict_image
predict_image("path_to_new_image.jpg", model)
```
### **🔹 Example Output:**
```plaintext
Predicted: Poisonous 🐍 (Confidence: 0.87)
```
or
```plaintext
Predicted: Non-Poisonous ✅ (Confidence: 0.93)
```
---
## 🛠️ Tools & Technologies Used
🔹 **Programming:** Python
🔹 **Frameworks:** TensorFlow, Keras
🔹 **Libraries:** OpenCV, NumPy, Matplotlib
🔹 **Database & Storage:** Local Storage
🔹 **Version Control:** Git
---
## 📍THE TRAINED MODEL HASN'T BEEN ADDED DUE TO ITS EXCESSIVE FILE SIZE
## 📜 License
This project is licensed under the [**MIT License**](https://github.com/Anis196/Snk-bite-det/blob/main/LICENSE).
---
## 📬 Contact
For any queries, reach out at ✉️ [shaikhanis2004@gmail.com](mailto:shaikhanis2004@gmail.com).
---