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---
title: S15
emoji: 🐨
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 4.28.0
app_file: app.py
pinned: false
license: mit
---
# Inference of Vehicle detection using Yolov9
- This application showcases the inference capabilities of a Yolo v9 trained on the vehicle dataset from kaggle.
[Vehicle Dataset Repo Link](https://www.kaggle.com/datasets/nadinpethiyagoda/vehicle-dataset-for-yolo)
- The model is trained on 6 classes:
- car
- threewheel
- bus
- truck
- motorbike
- van
- The architecture is based on Yolo v9 papar https://arxiv.org/abs/2402.13616 and model is
trained using https://github.com/WongKinYiu/yolov9.git
- detect.py file used for inference.
- From gradio applicaiton call is made to detect.py using command line shell with unique folder name passed as argument
- After processing, image/video is picked from same location.
Mentioned below is the link for Training Repository [Training Repo Link](https://github.com/Shivdutta/ERA2-Session15-Yolov9)
- Post training process, the model is saved locally and then uploaded to Gradio Spaces.
- Attached below is the link to [download model file](https://huggingface.co/spaces/Shivdutta/S15-YOLOV9/blob/main/yolov9/runs/train/exp/weights/best.pt)
- This app has two features :
- **Video Prediction:**
" - This feature will allow detection of moving vehicles in the the video
- **Image Prediction:**
- This feature will allow detection of vehicle in the the image
## Usage:
- **Video Prediction:**
" - Upload video file and detect vehicles present in the video.
- Inferencing is done using CPU therefore it takes more time.
- **Image Prediction:**
- Upload image file and detect vehicles present in the image.
![Prediction Output](Img-1.png)
![Prediction Output](Img-2.png)
![Prediction Output](Img-3.png)
![Prediction Output](Img-4.png)
## Training repo:
https://github.com/Shivdutta/ERA2-Session15-Yolov9
Thank you
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