https://github.com/himanchalchandra/humanactivityrecognition

Human Activity Recognition
https://github.com/himanchalchandra/humanactivityrecognition

artificial-intelligence deep-learning machine-learning opencv python

Last synced: about 1 month ago
JSON representation

Human Activity Recognition

• Host: GitHub
• URL: https://github.com/himanchalchandra/humanactivityrecognition
• Owner: HimanchalChandra
• Created: 2021-07-24T22:22:28.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-25T11:35:58.000Z (over 3 years ago)
• Last Synced: 2024-04-28T03:11:41.179Z (7 months ago)
• Topics: artificial-intelligence, deep-learning, machine-learning, opencv, python
• Language: Python
• Homepage:
• Size: 10.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# HumanActivityRecognition




## Task

This task involves creating a pipeline to test out inference from two different Human Activity recognition models (PyTorch and

TensorFlow) which have been trained on the Kinetics dataset.

## Installation 

### The code has been tested on Ubuntu 18.04 with Python 3.6

1. Install virtualenv

```

sudo apt install -y python3-venv

```

2. Clone the Repo

```

git clone https://github.com/HimanchalChandra/HumanActivityRecognition.git

```

3. Navigate to the sub-folder

```

cd HumanActivityRecognition

```

4. Initialize virtual environment 

```

python3 -m venv my_env

```

5. Activate the virtual environment

```

source my_env/bin/activate

```

6. Upgrade pip 

```

(my_env) pip install --upgrade pip

```

7. Install the dependencies

```

(my_env) pip install -r requirements.txt

```

8. Download the pre-trained models from [here](https://drive.google.com/file/d/1gb2d3WWFtKi87LRZ9udOb17O7-xPVHa_/view?usp=sharing), and extract the zip file in the HumanActivityRecognition folder itself.

9. For Pytorch Model use this script ('pyt' is the command line argument for PyTorch Model):

```

(my_env) python final_pipeline.py --video_root ./videos --model resnext-101-64f-kinetics.pth --type pyt

```

10. For TensorFlow Model use this script ('tf' is the command line argument for TensorFlow Model):

```

(my_env) python final_pipeline.py --video_root ./videos --model i3d-kinetics-400_1 --type tf

```

11. Refer Task_Writeup in this repo for detailed step-by-step procedure.

## Result

1. PyTorch Model:






 

 





3. TensorFlow Model: