https://github.com/oke-aditya/pytorch_fasterrcnn

Fine-Tune Pytorch Faster RCNN for your own task.
https://github.com/oke-aditya/pytorch_fasterrcnn

deep-learning detection pytorch pytorch-rcnn torchvision

Last synced: 16 days ago
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Fine-Tune Pytorch Faster RCNN for your own task.

• Host: GitHub
• URL: https://github.com/oke-aditya/pytorch_fasterrcnn
• Owner: oke-aditya
• License: gpl-3.0
• Created: 2020-05-24T16:03:50.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-12-02T08:34:28.000Z (almost 4 years ago)
• Last Synced: 2024-10-12T11:06:41.944Z (about 1 month ago)
• Topics: deep-learning, detection, pytorch, pytorch-rcnn, torchvision
• Language: Python
• Homepage: https://oke-aditya.github.io/pytorch_fasterrcnn/
• Size: 545 KB
• Stars: 17
• Watchers: 2
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Pytorch Faster RCNN

- Note that this PyPi package is no longer maintained. It will work fine upto this release, but I won't do bug fixes.

- The entire code and API is migrated to [Quickvison](https://github.com/Quick-AI/quickvision), it has similar API and is maintained actively.

![CI Tests](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/CI%20Tests/badge.svg)

![Check Formatting](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/Check%20Formatting/badge.svg)

![Build mkdocs](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/Build%20mkdocs/badge.svg)

![Deploy mkdocs](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/Deploy%20mkdocs/badge.svg)

![PyPi Release](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/PyPi%20Release/badge.svg)

![Install Package](https://github.com/oke-aditya/pytorch_fasterrcnn/workflows/Install%20Package/badge.svg)

Faster RCNN Fine-Tune Implementation in Pytorch.

## How to use ?

1. git clone the repo

```

git clone https://github.com/oke-aditya/pytorch_fasterrcnn.git

```

2. Install PyTorch and torchvision for your system.

Simply edit the config file to set your hyper parameters.

3. Keep the training and validation csv file as follows

NOTE

Do not use target as 0 class. It is reserved as background.

```

image_id xtl ytl xbr ybr      target

1        xmin ymin xmax ymax   1

1        xmin ymin xmax ymax   2

2		 xmin ymin xmax ymax   3

```

4. Simply edit the config file to set your hyper parameters

5. Run the train.py file

# Features: -

- It works for multiple class object detection.

## Backbones Supported: -

- Note that backbones are pretrained on imagenet. 

- Following backbones are supported

1. vgg11, vgg13, vgg16, vgg19

2. resnet18, resnet34, resnet50, resnet101, resnet152

3. renext101

4. mobilenet_v2

Sample Outputs

# Helmet Detector

![Helmet Detection](outputs/helmet.jpg)

# Mask Detector

![Mask Detection](outputs/mask.jpg)

If you like the implemenation or have taken an inspiration do give a star :-)