1. Data Collection
3. Data Preprocessing
1. Data Collection
Gather a large dataset of images of birds of different species. This dataset includes images of birds from various angles, in different environments and lighting conditions, etc.
2. Data Labeling
3. Data Preprocessing
1. Data Collection
Label each image with the species of bird. This is a crucial step performed by our chief birder, Jerar, and requires expertise in bird species identification.
3. Data Preprocessing
3. Data Preprocessing
3. Data Preprocessing
Clean and preprocess the data to remove any irrelevant information, resize images to a consistent size, and convert them to a format suitable for use in machine learning models.
4. Feature Extraction
4. Feature Extraction
3. Data Preprocessing
Meaningful features in the images can be used to identify different bird species. This would involve using computer vision techniques to detect key features, such as the shape of the bird's beak, the pattern of its feathers, etc.
5. Model Training
4. Feature Extraction
6. Model Evaluation
Train a machine learning model using the labeled and preprocessed data. A popular approach for image classification tasks is Convolutional Neural Networks (CNNs), which are a type of deep learning algorithm.
6. Model Evaluation
4. Feature Extraction
6. Model Evaluation
We will evaluate the performance of the trained model on a separate dataset that it has not seen before. This will give us an idea of how well the model is able to adapt to new data.
7. Model Deployment
7. Model Deployment
7. Model Deployment
Deployment of the trained model in a suitable environment for use in a real-world scenario. This would involve integrating the model into a mobile app