Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->
- Developed by: Haaris
- Model type: Neural Network Model for Image
- License: MIT
Model Sources [optional]
<!-- Provide the basic links for the model. -->
- Demo [optional]: [More Information Needed]
Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This collection of model demonstrate different Neural Network model
Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Can be directly use with Pytorch just load the model pass image with 28*28 pixel with one color channel and it will able to give classification between two.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
MNIST handwritten digits are being used in this application as dataset where 60,000 images are used in training set and 10,000 images were used in testing set.
Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Testing Data, Factors & Metrics
Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]