Upload 4 files
Browse files- .gitattributes +1 -0
- app.py +34 -0
- best.torchscript +3 -0
- requirements.txt +9 -0
- test_image.jpg +0 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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best.torchscript filter=lfs diff=lfs merge=lfs -text
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app.py
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# -*- coding: utf-8 -*-
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"""app.ipynb
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1nIR_ak2SXq9fwQmKpq4eupLwQU_SI4fn
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"""
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import gradio as gr
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import torch
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import cv2
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from ultralytics import YOLO
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import numpy as np
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model = YOLO("best.torchscript")
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def predict(image):
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results = model(image)
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annotated = results[0].plot()
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annotated_rgb = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
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return annotated_rgb
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iface = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="numpy", label="Upload an image"),
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outputs=gr.Image(type="numpy", label="Detected image"),
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title="YOLOv8 Object Detection",
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description="Upload an image to detect objects using a YOLOv8 model in TorchScript format.",
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examples=None
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)
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if __name__ == "__main__":
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iface.launch()
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best.torchscript
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e3c0f08c1e1ba08508c3e929c8aba1bc16c066adab69db62c7db217c499edae
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size 13054046
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requirements.txt
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altair
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pandas
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streamlit
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ultralytics
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gradio
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wandb
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opencv-python-headless
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torch
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numpy
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test_image.jpg
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