Update README.md

README.md

@@ -1,13 +1,20 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- segments/sidewalk-semantic
+language:
+- en
+base_model:
+- facebook/maskformer-swin-base-coco
+pipeline_tag: image-segmentation
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This model is a fine-tuned version of MaskFormer-Swin-Base-Coco, trained on the Sidewalk Dataset for instance segmentation. 
+The original MaskFormer model is designed for dense prediction tasks like semantic segmentation and instance segmentation. 
+It leverages the power of the Swin Transformer, a powerful vision model, to capture both local and global contextual information for improved segmentation performance.
 
 ## Model Details
 
@@ -15,33 +22,20 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Developed by:** Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
+- **Model type:** Segmentation
+- **Finetuned from model [optional]:** MaskFormer-Swin-Base
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This fine-tuned MaskFormer-Swin-Base-Coco model is optimized for instance segmentation in urban environments, specifically sidewalks. 
+It can be used in applications such as smart city planning, autonomous vehicles, urban mobility, and surveillance systems, where accurate detection and 
+segmentation of pedestrians, street furniture, and obstacles are essential for improving navigation, safety, and city infrastructure analysis.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+can look for https://huggingface.co/facebook/maskformer-swin-base-coco for instructions
 
 ### Downstream Use [optional]