YOLOv7 / app.py
hiraltalsaniya's picture
Update app.py
a6f7bad
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from PIL import Image
REPO_ID = "hiraltalsaniya/YOLOv7"
FILENAME = "best.pt"
yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME,repo_type='space')
model = torch.hub.load('WongKinYiu/yolov7:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True)
def object_detection(im, size=416):
results = model(im)
results.render()
return Image.fromarray(results.imgs[0])
title = "Yolov7 Custom"
image = gr.inputs.Image(shape=(416, 416), image_mode="RGB", source="upload", label="Upload Image", optional=False)
outputs = gr.outputs.Image(type="pil", label="Output Image")
Custom_description="Custom Training Performed on colab style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> <b>1st</b> class is for Person Detected<br><b>2nd</b> class is for Car Detected"
Footer = (
"MOdel train on our custome dataset")
examples1=[["Image1.jpeg"],["Image2.jpeg"],["Image3.jpeg"],["Image4.jpeg"],["Image5.jpeg"],["Image6.jpeg"],["horses.jpeg"],["horses.jpeg"]]
Top_Title="<center>Yolov7 πŸš€ Custom Trained style='text-decoration: underline' target='_blank'></center></a>Face with mask and face without mask Detection"
css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}"
css = ".image-preview {height: auto !important;}"
gr.Interface(
fn=object_detection,
inputs=image,
outputs=outputs,
title=Top_Title,
description=Custom_description,
article=Footer,
examples=[["mask-person-2.jpg"], ["mask-person-2.jpg"]]).launch()