diff --git a/README.md b/README.md
index 013a7dc7..9729982c 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ Official PyTorch implementation of **YOLOv10**.
[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
-[](https://arxiv.org/abs/2405.14458) 
[](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web)
+[](https://arxiv.org/abs/2405.14458) [](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/jameslahm/YOLOv10) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web)
@@ -23,7 +23,7 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
## Notes
- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
-- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with other attributes, like class names and training args, for ease of use.
+- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with class names, for ease of use.
## UPDATES 🔥
- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
@@ -97,12 +97,10 @@ model = YOLOv10()
# If you want to finetune the model with pretrained weights, you could load the
# pretrained weights like below
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
-# Or
+# or
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
-# Note that you can upload your trained model to HuggingFace Hub like below
-# model.push_to_hub("reponame", config={"model": "yolov10n/s/m/b/l/x.yaml"})
```
## Push to hub to 🤗
@@ -143,7 +141,7 @@ yolo predict model=yolov10n/s/m/b/l/x.onnx
# End-to-End TensorRT
yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
-# Or
+# or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine
diff --git a/app.py b/app.py
index e40cc846..5598af6d 100644
--- a/app.py
+++ b/app.py
@@ -1,15 +1,23 @@
-# Ackownledgement: https://huggingface.co/spaces/kadirnar/Yolov10/blob/main/app.py
-# Thanks to @kadirnar
-
+import PIL.Image as Image
import gradio as gr
-from ultralytics import YOLOv10
-def yolov10_inference(image, image_size, conf_threshold):
- model = YOLOv10.from_pretrained("jameslahm/yolov10n")
-
- model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)
-
- return model.predictor.plotted_img[:, :, ::-1]
+from ultralytics import YOLOv10
+
+def predict_image(img, model_id, image_size, conf_threshold):
+ model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
+ results = model.predict(
+ source=img,
+ conf=conf_threshold,
+ show_labels=True,
+ show_conf=True,
+ imgsz=image_size,
+ )
+
+ for r in results:
+ im_array = r.plot()
+ im = Image.fromarray(im_array[..., ::-1])
+
+ return im
def app():
with gr.Blocks():
@@ -20,14 +28,14 @@ def app():
model_id = gr.Dropdown(
label="Model",
choices=[
- "yolov10n.pt",
- "yolov10s.pt",
- "yolov10m.pt",
- "yolov10b.pt",
- "yolov10l.pt",
- "yolov10x.pt",
+ "yolov10n",
+ "yolov10s",
+ "yolov10m",
+ "yolov10b",
+ "yolov10l",
+ "yolov10x",
],
- value="yolov10s.pt",
+ value="yolov10m",
)
image_size = gr.Slider(
label="Image Size",
@@ -40,16 +48,16 @@ def app():
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
- step=0.1,
+ step=0.05,
value=0.25,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
- output_image = gr.Image(type="numpy", label="Annotated Image")
+ output_image = gr.Image(type="pil", label="Annotated Image")
yolov10_infer.click(
- fn=yolov10_inference,
+ fn=predict_image,
inputs=[
image,
model_id,
@@ -63,18 +71,18 @@ def app():
examples=[
[
"ultralytics/assets/bus.jpg",
- "yolov10s.pt",
+ "yolov10s",
640,
0.25,
],
[
"ultralytics/assets/zidane.jpg",
- "yolov10s.pt",
+ "yolov10s",
640,
0.25,
],
],
- fn=yolov10_inference,
+ fn=predict_image,
inputs=[
image,
model_id,
@@ -82,7 +90,7 @@ def app():
conf_threshold,
],
outputs=[output_image],
- cache_examples=True,
+ cache_examples='lazy',
)
gradio_app = gr.Blocks()
@@ -93,8 +101,14 @@ with gradio_app:
YOLOv10: Real-Time End-to-End Object Detection
""")
+ gr.HTML(
+ """
+
+ """)
with gr.Row():
with gr.Column():
app()
-
-gradio_app.launch(debug=True)
\ No newline at end of file
+if __name__ == '__main__':
+ gradio_app.launch()
\ No newline at end of file