gradio web ui

README.md

@@ -51,6 +51,7 @@ $ pip install -r requirements.txt
 * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED
 * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW
 * [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)  🌟 NEW
+* [Gradio App](https://gradio.app/g/AK391/yolov5)  🌟 NEW
 * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
 * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW
 * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)

requirements.txt

@@ -27,3 +27,4 @@ pandas
 # extras --------------------------------------
 thop  # FLOPS computation
 pycocotools>=2.0  # COCO mAP
+gradio

utils/gradio/demo.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import torch
+from PIL import Image
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
+
+
+def yolo(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+title = "YOLOv5"
+description = "YOLOv5 demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \
+          "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \
+          "and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \
+          "<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
+
+examples = [['zidane.jpg'], ['bus.jpg']]
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples).launch(
+    debug=True)