Model Card for Model ID
"Welcome to our Indian Bird Classifier model repository! Our model is trained on a rich dataset comprising 800 images for each of the ten distinct species of Indian birds. Leveraging deep learning techniques, it accurately classifies bird species, aiding in wildlife conservation, ecological studies, and birdwatching. Join us in exploring the vibrant avian biodiversity of India with our specialized classification model!"
Model Details
Model Description
The model employs a Convolutional Neural Network (CNN) architecture, which is well-suited for handling image data due to its ability to automatically learn spatial hierarchies of features. It comprises multiple layers, including convolutional layers, pooling layers, and fully connected layers.
Convolutional Layers: These layers perform convolution operations on input images using learnable filters. The filters detect various features such as edges, textures, and patterns. The output of each convolutional layer consists of feature maps that represent the activation of different filters across the input image. Pooling Layers: Pooling layers downsample the feature maps obtained from convolutional layers, reducing their spatial dimensions. This helps in controlling the model's computational complexity and extracting the most important features while preserving their spatial relationships. Fully Connected Layers: These layers are typically placed at the end of the CNN architecture and are responsible for making final predictions. They take the flattened output from the preceding layers and perform classification tasks by applying learned weights and biases. The model is trained using a large dataset of Indian bird images, with each image labeled with its corresponding bird species. During training, the model learns to adjust its parameters (weights and biases) through the process of backpropagation, minimizing a predefined loss function such as categorical cross-entropy.
To enhance generalization and robustness, the model incorporates techniques such as data augmentation, which introduces variations in the training data by applying transformations like rotation, scaling, and flipping. This helps the model to learn invariant representations of the bird species, making it more resilient to variations in the input data.
After training, the model undergoes evaluation using a separate validation dataset to assess its performance metrics such as accuracy, precision, recall, and F1-score. Fine-tuning and hyperparameter tuning may be applied iteratively to optimize the model's performance further.
Once trained and validated, the model can be deployed for inference tasks, where it takes input images of Indian birds and outputs predictions regarding their species with high accuracy, contributing to various applications in wildlife conservation, ecological studies, and birdwatching.
- Developed by: Roshaun
- Model type: Convolution Neural Network (CNN) using Tensorflow
- Language(s) (NLP): [More Information Needed]
- License: -cuda
- Finetuned from model [optional]: Built from scratch
Model Sources [optional]
- Repository: https://github.com/roshnn24/FeatherFlock_AI
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- Demo [optional]: [More Information Needed]
Uses
Classifies 10 types of Indian Birds
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
https://drive.google.com/file/d/1HubUetnHU0Tx1IOlRiDdU9QwmXXmtdgW/view?usp=share_link
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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