Fix figures

README.md

@@ -15,10 +15,12 @@ pipeline_tag: image-classification
 
 # SuSy - Synthetic Image Detector
 
-![susy-logo](susy_logo.jpeg)
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/NobqlpFbFkTyBi1LsT9JE.png" alt="image" width="300" height="auto">
+
 
 - **Repository:** https://github.com/HPAI-BSC/SuSy
 - **Dataset:** https://huggingface.co/datasets/HPAI-BSC/SuSy-Dataset
+- **Paper:** TBD
 
 ## Model Details
 
@@ -26,7 +28,7 @@ pipeline_tag: image-classification
 
 SuSy is a Spatial-Based Synthetic Image Detection and Recognition Model, designed and trained to detect synthetic images and attribute them to a generative model (i.e., two StableDiffusion models, two Midjourney versions and DALL·E 3). The model takes image patches of size 224x224 as input, and outputs the probability of the image being authentic or having been created by each of the aforementioned generative models.
 
-![model-architecture](model_architecture.png)
+<img src="model_architecture.png" alt="image" width="900" height="auto">
 
 The model is based on a CNN architecture and is trained using a supervised learning approach. It's design is based on [previous work](https://upcommons.upc.edu/handle/2117/395959), originally intended for video superresolution detection, adapted here for the tasks of synthetic image detection and recognition. The architecture consists of two modules: a feature extractor and a multi-layer perceptron (MLP), as it's quite light weight. SuSy has a total of 12.7M parameters, with the feature extractor accounting for 12.5M parameters and the MLP accounting for the remaining 197K.