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https://github.com/IBM/MAX-Sports-Video-Classifier

Categorize sports videos according to which sport the video depicts.
https://github.com/IBM/MAX-Sports-Video-Classifier

docker-image machine-learning machine-learning-models tensorflow

Last synced: 25 days ago
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Categorize sports videos according to which sport the video depicts.

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[![Build Status](https://travis-ci.org/IBM/MAX-Sports-Video-Classifier.svg?branch=master)](https://travis-ci.org/IBM/MAX-Sports-Video-Classifier) [![Website Status](https://img.shields.io/website/http/max-sports-video-classifier.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-sports-video-classifier.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)



[](http://ibm.biz/max-to-ibm-cloud-tutorial)



# IBM Code Model Asset Exchange: Sports Video Classifier



This repository contains code to instantiate and deploy a video classification model. The model recognizes the 487 different classes of sports activities in the [Sports-1M Dataset](https://cs.stanford.edu/people/karpathy/deepvideo/). The model consists of a deep 3-D convolutional net that was trained on the Sports-1M dataset. The input to the model is a video, and the output is a list of estimated class probabilities.



The model is based on the [C3D TensorFlow Model](https://github.com/hx173149/C3D-tensorflow). The model files are hosted on [IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-sports-video-classifier/1.0.0/assets.tar.gz). The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).



## Model Metadata

| Domain | Application | Industry  | Framework | Training Data | Input Data Format |

| ------------- | --------  | -------- | --------- | --------- | -------------- | 

| Vision | Video Classification | General | TensorFlow | [Sports-1M](https://cs.stanford.edu/people/karpathy/deepvideo/) | Video (MPEG-4)| 



## References

* _D. Tran, L. Bourdev, R. Fergus, L. Torresani, M. Paluri_, [C3D: Generic Features for Video Analysis](http://vlg.cs.dartmouth.edu/c3d/)

* _A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, L. Fei-Fei_, ["Large-scale Video Classification with Convolutional Neural Networks"](https://cs.stanford.edu/people/karpathy/deepvideo/deepvideo_cvpr2014.pdf)

* [Sports-1M Dataset Project Page](https://cs.stanford.edu/people/karpathy/deepvideo/)

* [C3D TensorFlow Model](https://github.com/hx173149/C3D-tensorflow)



## Licenses



| Component | License | Link  |

| ------------- | --------  | -------- |

| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |

| Model Weights | [MIT](https://opensource.org/licenses/MIT) | [C3D-TensorFlow](https://github.com/hx173149/C3D-tensorflow) |

| Model Code (3rd party) | [MIT](https://opensource.org/licenses/MIT) | [C3D-TensorFlow](https://github.com/hx173149/C3D-tensorflow) |

| Test assets | Various | [Asset README](samples/README.md) |



## Pre-requisites:



* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.

* The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

* If you are on x86-64/AMD64, your CPU must support [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) at the minimum.



# Deployment options



* [Deploy from Quay](#deploy-from-quay)

* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)

* [Deploy on Kubernetes](#deploy-on-kubernetes)

* [Run Locally](#run-locally)



## Deploy from Quay



To run the docker image, which automatically starts the model serving API, run:



```

$ docker run -it -p 5000:5000 quay.io/codait/max-sports-video-classifier

```



This will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it.

If you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.



## Deploy on Red Hat OpenShift



You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI [in this tutorial](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), specifying `quay.io/codait/max-sports-video-classifier` as the image name.



## Deploy on Kubernetes



You can also deploy the model on Kubernetes using the latest docker image on Quay.



On your Kubernetes cluster, run the following commands:



```

$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Sports-Video-Classifier/master/max-sports-video-classifier.yaml

```



The model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.



A more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).



## Run Locally



1. [Build the Model](#1-build-the-model)

2. [Deploy the Model](#2-deploy-the-model)

3. [Use the Model](#3-use-the-model)

4. [Development](#4-development)

5. [Cleanup](#5-cleanup)



### 1. Build the Model



Clone this repository locally. In a terminal, run the following command:



```

$ git clone https://github.com/IBM/MAX-Sports-Video-Classifier.git

```



Change directory into the repository base folder:



```

$ cd MAX-Sports-Video-Classifier

```



To build the docker image locally, run: 



```

$ docker build -t max-sports-video-classifier .

```



All required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU only (we will add support for GPU images later).



### 2. Deploy the Model



To run the docker image, which automatically starts the model serving API, run:



```

$ docker run -it -p 5000:5000 max-sports-video-classifier

```



### 3. Use the Model



The API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load it. From there you can explore the API and also create test requests.



Use the `model/predict` endpoint to load a test video file and get predicted labels for the video from the API.



![Swagger Doc Screenshot](docs/swagger-screenshot.png)



You can also test it on the command line, for example:



```bash

$ curl -F "video=@samples/basketball.mp4" -XPOST http://localhost:5000/model/predict

```



```json

{

  "status": "ok",

  "predictions": [

    {

      "label_id": "367",

      "label": "basketball",

      "probability": 0.39916181564331

    },

    {

      "label_id": "370",

      "label": "streetball",

      "probability": 0.16513635218143

    },

    {

      "label_id": "369",

      "label": "3x3 (basketball)",

      "probability": 0.11865037679672

    }

  ]

}

```



### 4. Development



To run the Flask API app in debug mode, edit `config.py` to set `DEBUG = True` under the application settings. You will then need to rebuild the docker image (see [step 1](#1-build-the-model)).



### 5. Cleanup



To stop the Docker container, type `CTRL` + `C` in your terminal.



## Resources and Contributions

   

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions [here](https://github.com/CODAIT/max-central-repo).