Update README.md

README.md

@@ -19,13 +19,6 @@ SegFormer model with a MiT-B2 backbone fine-tuned on Coralscapes at resolution 1
 
 ### Model Description
 
-Training is conducted following the Segformer original [implementation](https://proceedings.neurips.cc/paper_files/paper/2021/file/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf), using a batch size of 8 for 265 epochs, 
-using the AdamW optimizer with an initial learning rate of 6e-5, weight decay of 1e-2 and polynomial learning rate scheduler with a power of 1. 
-During training, images are randomly scaled within a range of 1 and 2, flipped horizontally with a 0.5 probability and randomly cropped to 1024×1024 pixels. 
-Input images are normalized using the ImageNet mean and standard deviation. For evaluation, a non-overlapping sliding window strategy is employed, 
-using a window size of 1024x1024. 
-
-
 - **Model type:** SegFormer
 - **License:** [More Information Needed]
 - **Finetuned from model:** [SegFormer (b2-sized) encoder pre-trained-only (`nvidia/mit-b2`)](https://huggingface.co/nvidia/mit-b2)