.

README.md

@@ -6,7 +6,8 @@ sdk: gradio
 sdk_version: 4.44.0
 app_file: app.py
 pinned: false
-license: agpl-3.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Get `yolov2.weights` from https://pjreddie.com/darknet/yolov2/.
+
+Get `model/` from https://pjreddie.com/darknet/install/ building it on a machine with the same architecture as the machine where it is deployed.

app.py

@@ -1,17 +1,14 @@
 import os
 
 import gradio as gr
-import PIL.Image as Image
-
-from ultralytics import ASSETS, YOLO
 
 model = None
 
 
-def predict_image(img, conf_threshold, iou_threshold, model_name):
+def predict_image(img, conf_threshold):
     """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
     os.chdir('/home/user/app/model')
-    os.system(f'/home/user/app/model/darknet detect /home/user/app/model/cfg/yolov2.cfg /home/user/app/model/yolov2.weights {img}')
+    os.system(f'/home/user/app/model/darknet detect /home/user/app/model/cfg/yolov2.cfg /home/user/app/model/yolov2.weights {img} -thresh {conf_threshold}')
 
     return '/home/user/app/model/predictions.jpg'
 
@@ -21,12 +18,10 @@ iface = gr.Interface(
     inputs=[
         gr.Image(type="filepath", label="Upload Image"),
         gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
-        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
-        gr.Radio(choices=["yolo11n", "yolo11s", "yolo11n-seg", "yolo11s-seg", "yolo11n-pose", "yolo11s-pose"], label="Model Name", value="yolo11n"),
     ],
     outputs=gr.Image(type="filepath", label="Result"),
-    title="Ultralytics Gradio Application 🚀",
-    description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
+    title="OneThousand YoloV2 Person Detection",
+    description="Upload images for object detecting using YoloV2 model (https://pjreddie.com/darknet/yolov2/). Inference can take up to 15s.",
 )
 
 iface.launch()