Bangladeshi Venomous Snakes Image Dataset
Description
The dataset of venomous Bangladeshi snakes, comprising images of Russell's Viper, King Cobra, and Common Krait, offers a valuable resource for developing an image processing-based deep learning algorithm. This algorithm can aid in the rapid identification of these snakes, enhancing both ecological preservation and public safety. The comprehensive methods and protocols described ensure that the data collection and processing are rigorous, enabling others to reproduce and build upon this research effectively.
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Steps to reproduce
The dataset consists of images of three venomous snake species commonly found in Bangladesh: Russell's Viper, King Cobra, and Common Krait. These species are significant due to their prevalence and the high mortality rate associated with their bites. The development of an image processing-based deep learning or machine learning algorithm using this dataset can potentially lead to the creation of an application capable of identifying these snakes. Such an application could alert the emergency authorities and the forest department, contributing to both ecological preservation and public safety. Additionally, snake venom is valuable for medical drug development, underscoring the importance of accurate snake identification. Data Splitting: Training Set: 70% of the images. Test Set: 30% of the images. Machine Learning Workflow Model Selection: Frameworks: TensorFlow, Keras, or PyTorch. Models: Convolutional Neural Networks (CNNs) like VGG16, ResNet50, or custom architectures. Training: Environment: High-performance computing resources or cloud platforms with GPU support. Hyperparameters: Learning rate, batch size, epochs, and optimizer settings. Evaluation: Metrics: Accuracy, precision, recall, and F1-score. Tools: Confusion matrices and ROC curves to assess model performance. Deployment: Platform: Mobile application (iOS/Android) or web application. Backend: Integration with cloud services for real-time image processing and model inference.