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license: c-uda |
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# Model Card for Model ID |
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"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!" |
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## Model Details |
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### Model Description |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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- **Developed by:** Roshaun |
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- **Model type:** Convolution Neural Network (CNN) using Tensorflow |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** -cuda |
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- **Finetuned from model [optional]:** Built from scratch |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/roshnn24/FeatherFlock_AI |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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Classifies 10 types of Indian Birds |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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https://drive.google.com/file/d/1HubUetnHU0Tx1IOlRiDdU9QwmXXmtdgW/view?usp=share_link |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
