feat: ✨ Attempt ZeroGPU usage and NMS slider added

Signed-off-by: Onuralp SEZER <[email protected]>

app.py

@@ -16,6 +16,7 @@ from torchvision.ops import nms
 import supervision as sv
 from PIL import Image
 import cv2
+import spaces
 
 import gradio as gr
 
@@ -31,7 +32,7 @@ Annototions Powered by [Supervision](https://github.com/roboflow/supervision).
 
 EXAMPLES = [
     ["https://media.roboflow.com/efficient-sam/corgi.jpg", "dog",0.5,0.5,0.5,100],
-    ["https://media.roboflow.com/efficient-sam/horses.jpg", "horse",0.5,0.5,0.5,100],
+    ["https://media.roboflow.com/efficient-sam/horses.jpg", "horses",0.5,0.5,0.5,100],
     ["https://media.roboflow.com/efficient-sam/bears.jpg", "bear",0.5,0.5,0.5,100],
 ]
 
@@ -53,12 +54,13 @@ def load_runner():
     runner.model.eval()
     return runner
 
+@spaces.GPU
 def run_image(
         input_image,
         class_names="person,car,bus,truck",
         score_thr=0.05,
         iou_thr=0.5,
-        nms_thr = 0.5,
+        nms_thr=0.5,
         max_num_boxes=100,
         ):
     runner = load_runner()
@@ -137,11 +139,24 @@ iou_threshold_component = gr.Slider(
         "detections."
     ))
 
+nms_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.5,
+    step=0.01,
+    label="NMS Threshold",
+    info=(
+        "The Non-Maximum Suppression (NMS) Threshold is a parameter that determines the Intersection over Union (IoU) threshold for suppressing bounding boxes. "
+        "A lower value will reduce the likelihood of overlapping bounding boxes, resulting in a more stringent detection process. Conversely, a higher value "
+        "will permit more overlapping bounding boxes, thereby allowing for a wider variety of detections."
+    ))
+
 with gr.Blocks() as demo:
     gr.Markdown(TITLE)
     with gr.Accordion("Configuration", open=False):
         confidence_threshold_component.render()
         iou_threshold_component.render()
+        nms_threshold_component.render()
     with gr.Tab(label="Image"):
         with gr.Row():
             input_image_component = gr.Image(
@@ -171,6 +186,7 @@ with gr.Blocks() as demo:
                 image_categories_text_component,
                 confidence_threshold_component,
                 iou_threshold_component,
+                nms_threshold_component
             ],
             outputs=output_image_component
         )
@@ -183,6 +199,7 @@ with gr.Blocks() as demo:
             image_categories_text_component,
             confidence_threshold_component,
             iou_threshold_component,
+            nms_threshold_component
         ],
         outputs=output_image_component
     )