edit texts and layout

app.py

@@ -186,46 +186,46 @@ def process_image_with_BMP(
 
 
 with gr.Blocks() as app:
-    gr.Markdown("# Small Object Detection with SAHI + YOLOv11")
+    gr.Markdown("# BBoxMaskPose Image Demo")
     gr.Markdown(
-        "SAHI + YOLOv11 demo for small object detection. "
-        "Upload your own image or click an example image to use."
+        "Official demo for paper **Detection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle.** (ICCV 2025)"
+    )
+    gr.Markdown(
+        "For details, see the [project website](https://mirapurkrabek.github.io/BBox-Mask-Pose/) or [arXiv paper](https://arxiv.org/abs/2412.01562)."
+        "The demo showcases the capabilities of the BBoxMaskPose framework. If you want to play around with parameters, use the [GitHub demo](https://github.com/MiraPurkrabek/BBoxMaskPose)."
     )
 
     with gr.Row():
         with gr.Column():
             original_image_input = gr.Image(type="numpy", label="Original Image")
-            # bbox_thr_slider = gr.Slider(
-            #     minimum=0.0,
-            #     maximum=1.0,
-            #     step=0.01,
-            #     value=0.5,
-            #     label="BBox Confidence Threshold",
-            # )
-            # bbox_nms_slider = gr.Slider(
-            #     minimum=0.0,
-            #     maximum=1.0,
-            #     step=0.01,
-            #     value=0.5,
-            #     label="BBox NMS Threshold",
-            # )
-            # kpt_conf_slider = gr.Slider(
-            #     minimum=0.0,
-            #     maximum=1.0,
-            #     step=0.01,
-            #     value=0.5,
-            #     label="Keypoint Confidence Threshold",
-            # )
             submit_button = gr.Button("Run Inference")
 
         with gr.Column():
-            output_standard = gr.Image(type="numpy", label="RTMDet-L bboxes + masks")
+            output_standard = gr.Image(type="numpy", label="RTMDet-L + MaskPose-B")
         
         with gr.Column():
-            output_sahi_sliced = gr.Image(type="numpy", label="BMP 2x masks + poses")
+            output_sahi_sliced = gr.Image(type="numpy", label="BBoxMaskPose")
 
     
     gr.Examples(
+        label="OCHuman examples",
+        examples=[
+            ["examples/004806.jpg"],
+            ["examples/004684.jpg"],
+            ["examples/004849.jpg"],
+            ["examples/004981.jpg"],
+            ["examples/005002.jpg"],
+            ["examples/005056.jpg"],
+        ],
+        inputs=[
+            original_image_input,
+        ],
+        outputs=[output_standard, output_sahi_sliced],
+        fn=process_image_with_BMP,
+        cache_examples=True,
+    )
+    gr.Examples(
+        label="In-the-wild examples",
         examples=[
             ["examples/prochazka_MMA.jpg"],
             ["examples/riner_judo.jpg"],
@@ -233,9 +233,9 @@ with gr.Blocks() as app:
             ["examples/tackle1.jpg"],
             ["examples/tackle2.jpg"],
             ["examples/tackle5.jpg"],
+            ["examples/floorball_SKV_3.jpg"],
             ["examples/santa_o_crop.jpg"],
             ["examples/floorball_SKV_2.jpg"],
-            ["examples/floorball_SKV_3.jpg"],
         ],
         inputs=[
             original_image_input,
@@ -244,6 +244,10 @@ with gr.Blocks() as app:
         fn=process_image_with_BMP,
         cache_examples=True,
     )
+    gr.Markdown(
+        "Please note that due to HuggingFace restrictions, the demo runs much slower than the original implementation."
+        "For faster inference, use the [GitHub demo](https://github.com/MiraPurkrabek/BBoxMaskPose)."
+    )
 
     submit_button.click(
         fn=process_image_with_BMP,