Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Hemg/cifake-real-and-ai-generated-synthetic-images
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+tags:
+- Diffusors
+- GanDetectors
+- Cifake
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model card provides comprehensive information about the model's architecture, training data, evaluation metrics, and environmental impact.
+
+## Model Details
+
+### Model Description
+
+This model is a pre-trained model for image classification, specifically designed for detecting fake images, including both real and AI-generated synthetic images. It utilizes the ViT (Vision Transformer) architecture for image classification tasks.
+
+- **Developed by:** [Author(s) Name(s)]
+- **Funded by [optional]:** [Funding Source(s)]
+- **Shared by [optional]:** [Organization/Individual(s) Sharing the Model]
+- **Model type:** Vision Transformer (ViT)
+- **Language(s) (NLP):** N/A
+- **License:** Apache License 2.0
+- **Finetuned from model [optional]:** [Base Pre-trained Model]
+
+### Model Sources [optional]
+
+- **Repository:** https://github.com/AashishKumar-3002/AIGuardVision.git
+
+## Uses
+
+### Direct Use
+
+This model can be directly used for classifying images as real or AI-generated synthetic images.
+
+### Downstream Use [optional]
+
+This model can be fine-tuned for specific image classification tasks related to detecting fake images in various domains.
+
+### Out-of-Scope Use
+
+The model may not perform well on tasks outside the scope of image classification, such as object detection or segmentation.
+
+## Bias, Risks, and Limitations
+
+The model's performance may be influenced by biases in the training data, leading to potential inaccuracies in classification.
+
+### Recommendations
+
+Users should be aware of potential biases and limitations when using the model for classification tasks, and additional data sources may be necessary to mitigate biases.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model:
+
+[Code Snippet for Model Usage]
+
+## Training Details
+
+### Training Data
+
+The model was trained on the CIFake dataset, which contains real and AI-generated synthetic images for training the classification model.
+
+### Training Procedure
+
+#### Preprocessing [optional]
+
+Data preprocessing techniques were applied to the training data, including normalization and data augmentation to improve model generalization.
+
+#### Training Hyperparameters
+
+- **Training regime:** Fine-tuning with a learning rate of 0.0000001
+- **Batch Size:** 64
+- **Epochs:** 100
+
+#### Speeds, Sizes, Times [optional]
+
+- **Training Time:** 1 hr 36 min
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The model was evaluated on a separate test set from the CIFake dataset.
+
+#### Factors
+
+The evaluation considered factors such as class imbalance and dataset diversity.
+
+#### Metrics
+
+Evaluation metrics included accuracy, precision, recall, and F1-score.
+
+### Results
+
+The model achieved an accuracy of [Accuracy] on the test set, with detailed metrics summarized in the following table:
+
+[Metrics Table]
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[Information on Model Examination, if available]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+The model architecture is based on the Vision Transformer (ViT) architecture, which uses self-attention mechanisms for image classification tasks.