IBM/MAX-Places365-CNN

Name: MAX-Places365-CNN

Owner: International Business Machines

Description: Image classifier for physical places/locations, based on the Places365-CNN Model

Created: 2018-03-09 23:14:19.0

Updated: 2018-05-12 06:17:46.0

Pushed: 2018-03-20 23:08:18.0

Homepage: null

Size: 1684

Language: Python

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README

IBM Code Model Asset Exchange: Places365-CNN

This repository contains code to instantiate and deploy an image classification model. This model recognizes the 365 different classes of scene/location in the Places365-Standard subset of the Places2 Dataset. The model is based on the Places365-CNN Model and consists of a pre-trained deep convolutional net using the ResNet architecture, trained on the ImageNet-2012 data set. The pre-trained model is then fine-tuned on the Places365-Standard dataset. The input to the model is a 224x224 image, and the output is a list of estimated class probilities.

The specific model variant used in this repository is the PyTorch Places365 ResNet18 Model. The model files are hosted on IBM Cloud Object Storage. 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.

Model Metadata

| Domain | Application | Industry | Framework | Training Data | Input Data Format | | ————- | ——– | ——– | ——— | ——— | ————– | | Vision | Image Classification | General | Pytorch | Places365 | Image (RGB/HWC)|

References

• B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million Image Database for Scene Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
• B. Zhou, A. Lapedriza, J. Xiao, A. Torralba and A. Oliva, “Learning Deep Features for Scene Recognition using Places Database”, Advances in Neural Information Processing Systems 27, 2014.
• K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, CoRR (abs/1512.03385), 2015.
• Places2 Project Page
• Places365-CNN GitHub Page

Licenses

| Component | License | Link | | ————- | ——– | ——– | | This repository | Apache 2.0 | LICENSE | | Model Weights | CC BY License | Places365-CNN| | Model Code (3rd party) | MIT | Places365-CNN| | Test assets | Various | Asset README |

Pre-requisites:

• docker: The Docker command-line interface. Follow the installation instructions for your system.
• The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

Steps

1. Build the Model
2. Deploy the Model
3. Use the Model
4. Development
5. Clean Up

1. Build the Model

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

t clone https://github.com/IBM/MAX-Places365-CNN.git

Change directory into the repository base folder:

 MAX-Places365-CNN

To build the docker image locally, run:

cker build -t max-pytorch-places365 .

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:

cker run -it -p 5000:5000 max-pytorch-places365

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 image (you can use one of the test images from the assets folder) and get predicted labels for the image from the API.

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

rl -F "image=@assets/acquarium.jpg" -XPOST http://127.0.0.1:5000/model/predict

You should see a JSON response like that below:

tatus": "ok",
redictions": [
{
  "label_id": "9",
  "label": "aquarium",
  "probability": 0.97350615262985
},
{
  "label_id": "342",
  "label": "underwater\/ocean_deep",
  "probability": 0.0062678409740329
},
{
  "label_id": "297",
  "label": "science_museum",
  "probability": 0.005441018845886
},
{
  "label_id": "239",
  "label": "natural_history_museum",
  "probability": 0.00413528829813
},
{
  "label_id": "167",
  "label": "grotto",
  "probability": 0.0024146677460521
}

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).