Add viz

README.md

@@ -18,6 +18,11 @@ tags:
 
 ## **Model Overview**
 
+![viz.png](viz.png)
+*Preview of the model output on the example image.*
+
+The input of this model is expected to be a chart image. You can use the [Nemoretriever Page Element v3](https://huggingface.co/nvidia/nemoretriever-page-elements-v3) to detect and crop such images.
+
 ### **Description**
 
 The **NeMo Retriever Graphic Elements v1** model is a specialized object detection system designed to identify and extract key elements from charts and graphs. Based on YOLOX, an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights.
@@ -73,7 +78,7 @@ The **NeMo Retriever Graphic Elements v1** is designed for automating extraction
 **Architecture Type**: YOLOX <br>
 **Network Architecture**: DarkNet53 Backbone \+ FPN Decoupled head (one 1x1 convolution \+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction). YOLOX is a single-stage object detector that improves on Yolo-v3. <br>
 **This model was developed based on the Yolo architecture** <br>
-**Number of model parameters**: $5.4*10^7$ <br>
+**Number of model parameters**: 5.4e7 <br>
 
 ### Input
 
@@ -173,12 +178,10 @@ plt.show()
 Note that this repository only provides minimal code to infer the model.
 If you wish to do additional training, [refer to the original repo](https://github.com/Megvii-BaseDetection/YOLOX).
 
-<!---
 3. Advanced post-processing
 
 Additional post-processing might be required to use the model as part of a data extraction pipeline. 
 We provide examples in the notebook `Demo.ipynb`.
---->
 
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 ### Software Integration