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import gradio as gr
import yolov5
models = {
"yolov5s": yolov5.load("akbojda/yolov5s-aquarium"),
"yolov5m": yolov5.load("akbojda/yolov5m-aquarium"),
"yolov5l": yolov5.load("akbojda/yolov5l-aquarium"),
}
def predict(img, model_type):
model = models[model_type]
results = model(img, size=640)
detection_img = results.render()[0]
return detection_img
# Interface
inputs = [
gr.Image(),
gr.Dropdown(["yolov5s", "yolov5m", "yolov5l"],
label="Model",
value="yolov5s"),
]
outputs = [
gr.Image(elem_classes="output-image")
]
examples = [
["examples/ex1.jpg", None],
["examples/ex2.jpg", None],
["examples/ex3.jpg", None],
["examples/ex4.jpg", None],
["examples/ex5.jpg", None],
["examples/ex6.jpg", None],
]
title = "Aquarium object detection"
description = """
Based on [Aquarium Combined dataset](https://universe.roboflow.com/brad-dwyer/aquarium-combined)
Supports following 7 classes: [fish, jellyfish, penguin, puffin, shark, starfish, stingray]
"""
iface = gr.Interface(fn=predict,
inputs=inputs,
outputs=outputs,
examples=examples,
cache_examples=False,
title=title,
description=description)
iface.launch()
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