Update README.md

README.md

@@ -16,9 +16,11 @@ With its advanced reasoning capabilities and superior performance on geospatial
 
 ## Model Details
 
-- **Model architecture**: Inspired by LISA ![Lai et al., 2024](https://arxiv.org/pdf/2308.00692), LISAT integrates a multimodal large language model (LLM) with a segmentation model. Its architechture is shown below.
+- **Model architecture**: Inspired by LISA (Lai et al., 2024), LISAT integrates a multimodal large language model (LLM) with a segmentation model. Its architechture is shown below.
 
-![LISAT Model Architecture](https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png)
+<!-- ![LISAT Model Architecture](https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png) -->
+
+<img src="https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png" width="600"/>
 
 - **Training data**: we introduce the Geospatial Reasoning Segmentation Dataset (GRES), a collection of vision and language data designed around
 remote-sensing applications. GRES consists of two core components: PreGRES, a dataset consisting of over 1M remote-sensing specific visual instruction-tuning Q/A pairs for pre-training geospatial models, and GRES, a semi-synthetic dataset specialized for reasoning segmentation of remote-sensing data and consisting of 9,205 images and 27,615 natural language queries/answers within those images. From this LISAt dataset, we generate train, test, and validation splits consisting of 7,205, 1,500, and 500 images respectively.