Update README.md

README.md

@@ -1,8 +1,13 @@
 ---
+datasets:
+- Borcherding/Depth2RobotsV2_Annotations
 tags:
 - depth-to-robot
 - image-to-image
 - cyclegan
+- depth-to-anything
+base_model:
+- keras-io/CycleGAN
 ---
 
 # cycleGAN_Depth2RobotsV2 Model
@@ -22,7 +27,7 @@ This model transforms depth maps into robot-style images, and also transforms ro
 
 ## Model Description
 
-- This model was trained robot images generated with SDXL, and their associated depth maps take with Depth Anything V2:
+- This model was trained robot images generated with SDXL, and their associated depth maps were taken with Depth Anything V2:
 [Depth2RobotsV2_Annotations](https://huggingface.co/datasets/Borcherding/Depth2RobotsV2_Annotations)
 -   using CycleGAN architecture
 - It supports bidirectional transformation:
@@ -34,8 +39,8 @@ This model transforms depth maps into robot-style images, and also transforms ro
 
 ```bash
 # Clone the repository
-git clone https://github.com/yourusername/depth2robot
-cd depth2robot
+git clone https://huggingface.co/Borcherding/cycleGAN_Depth2RobotsV2
+cd cycleGAN_Depth2RobotsV2
 
 # Install dependencies
 pip install torch torchvision gradio pyvirtualcam
@@ -160,7 +165,7 @@ def download_model(direction="depth2image"):
         filename = "latest_net_G_B.pth"
     
     model_path = hf_hub_download(
-        repo_id="Borcherding/depth2AnythingCycleGAN_RobotsV2", 
+        repo_id="Borcherding/cycleGAN_Depth2RobotsV2", 
         filename=filename
     )
     return model_path
@@ -236,10 +241,10 @@ def transform_image(input_image_path, direction="depth2image"):
     return output_image
 ```
 
-## Model Checkpoints
+## Model Checkpoints 
 
 The model checkpoints are available on Hugging Face:
-- Repository: [Borcherding/depth2AnythingCycleGAN_RobotsV2](https://huggingface.co/Borcherding/depth2AnythingCycleGAN_RobotsV2)
+- Repository: [Borcherding/Depth2RobotsV2_Annotations](https://huggingface.co/datasets/Borcherding/Depth2RobotsV2_Annotations)
 - Files:
   - `latest_net_G_A.pth` - Generator for Depth to Robot Image transformation
   - `latest_net_G_B.pth` - Generator for Robot Image to Depth transformation
@@ -271,4 +276,4 @@ The integrated application (`integrated-depth-cyclegan.py`) also leverages [Dept
 
 - This model uses CycleGAN architecture from the paper [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593) by Zhu et al.
 - The implementation is based on [junyanz/pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)
-- Integrated application leverages Depth Anything V2 for depth estimation
+- Integrated application leverages Depth Anything V2 for depth estimation