Update README.md

README.md

@@ -25,9 +25,7 @@ With its advanced reasoning capabilities and superior performance on geospatial
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 - **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.
-
-The GRES dataset can be downloaded ![here](https://huggingface.co/datasets/jquenum/GRES/tree/main).
+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. The GRES dataset can be downloaded [here](https://huggingface.co/datasets/jquenum/GRES/tree/main).
 
 - **Implementation Details**: LISAT and LISATPRE are trained on eight DGX A100 80GB GPUs. In the first stage, we pretrain LISATPRE (context length = 2048) using LoRA for 1 epoch on PreGRES with next-token prediction cross-entropy loss. We employ the AdamW optimizer with a learning rate of 3e−4 and a cosine-decay learning rate scheduler, setting the batch size to 2 and gradient accumulation
 steps to 6.