Update README.md

README.md

@@ -1,37 +1,64 @@
----
-datasets:
-- Hemg/AI-Generated-vs-Real-Images-Datasets
-metrics:
-- accuracy
-base_model:
-- microsoft/resnet-50
-pipeline_tag: image-classification
-tags:
-- art
----
 
 # Multi-Task Image Classifier
 
-
 ## Model Overview
 This model is a **Multi-Task Image Classifier** that performs two tasks simultaneously:
 1. **Object Recognition:** Identifies the primary object in an image (e.g., "cat," "dog," "car," etc.).
 2. **Authenticity Classification:** Determines whether the image is AI-generated or a real photograph.
 
-The model uses a ResNet-50 backbone with two heads: one for multi-class object recognition (trained on pseudo-labels generated by a Vision Transformer) and another for binary classification (AI-generated vs. Real). It was trained on a subset of the [Hemg/AI-Generated-vs-Real-Images-Datasets](https://huggingface.co/datasets/Hemg/AI-Generated-vs-Real-Images-Datasets).
+The model uses a **ResNet-50** backbone with two heads: one for multi-class object recognition (trained on pseudo-labels generated by a Vision Transformer) and another for binary classification (AI-generated vs. Real). It was trained on a subset of the [Hemg/AI-Generated-vs-Real-Images-Datasets](https://huggingface.co/datasets/Hemg/AI-Generated-vs-Real-Images-Datasets).
 
 ## Intended Use
 This model is designed for:
-- **Digital Content Verification:** Detecting AI-generated images to prevent misinformation.
+- **Digital Content Verification:** Detecting AI-generated images to help prevent misinformation.
 - **Social Media Moderation:** Automatically flagging images that are likely AI-generated.
-- **Content Analysis:** Helping researchers understand the prevalence of AI art versus real images.
+- **Content Analysis:** Assisting researchers in understanding the prevalence of AI art versus real images in digital media.
 
 ## How to Use
-You can use this model locally or via a Hugging Face Space. For local usage, load the state dictionary into the model architecture using PyTorch. For example:
+You can use this model locally or via the provided Hugging Face Space. For local usage, load the state dictionary into the model architecture using PyTorch. For example:
 ```python
 import torch
-from model import MultiTaskModel  # your model definition
+from model import MultiTaskModel  # Your model definition
 
+# Instantiate your model architecture (must match training)
 model = MultiTaskModel(...)
+
+# Load the saved state dictionary (trained weights)
 model.load_state_dict(torch.load("multitask_model_weights.pth", map_location="cpu"))
-model.eval()
+model.eval()
+```
+Alternatively, you can test the model directly via our interactive demo:
+[Test the Model Here](https://huggingface.co/spaces/Abdu07/multitask-demo)
+
+## Training Data and Evaluation
+- **Dataset:** The model was trained on a subset of the [Hemg/AI-Generated-vs-Real-Images-Datasets](https://huggingface.co/datasets/Hemg/AI-Generated-vs-Real-Images-Datasets) comprising approximately 152k images.
+- **Metrics:**
+  - **Authenticity (AI vs. Real):** Validation accuracy reached around 85% after early epochs.
+  - **Object Recognition:** Pseudo-label accuracy started at around 38–40% and improved during training.
+- **Evaluation:** Detailed evaluation metrics and loss curves are available in our training logs.
+
+## Model Details
+- **Trained by:** [Abdellahi El Moustapha](https://abmstpha.github.io/)
+- **Language:** Not applicable (image model)
+- **Base Model:** ResNet-50
+- **Datasets:** Hemg/AI-Generated-vs-Real-Images-Datasets
+- **Library:** PyTorch
+- **Pipeline Tag:** image-classification
+- **Metrics:** Accuracy for both binary classification and multi-class object recognition
+- **Version:** v1.0
+
+## Limitations and Ethical Considerations
+- **Pseudo-Labeling:** The object recognition task uses pseudo-labels generated from a pretrained model, which may introduce noise or bias.
+- **Authenticity Sensitivity:** The binary classifier may face challenges with highly realistic AI-generated images.
+- **Usage:** This model is intended for research and prototyping purposes. Additional validation is recommended before deploying in high-stakes applications.
+
+## How to Cite
+If you use this model, please cite:
+```bibtex
+@misc{multitask_classifier,
+  title={Multi-Task Image Classifier},
+  author={Abdellahi El Moustapha},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Abdu07/multitask-model}}
+}
+```