--- license: c-uda --- # 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!" This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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 - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** Tensorflow CNN - **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 - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses Classifies 10 types of Indian Birds ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### 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. [More Information Needed] ## Training Details ### Training Data https://drive.google.com/file/d/1HubUetnHU0Tx1IOlRiDdU9QwmXXmtdgW/view?usp=share_link [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]