liquidinvestigations/image-classification-service repository overview

⁠Liquid Investigations

This Repository is part of Liquid Investigations.

• Source Code for image-classification-service⁠
• Project Home Page⁠
• Security⁠
• Contribution Guide⁠
• Code of Conduct⁠

⁠image-classification-service

REST service with image classification and object detection

⁠Usage

The service supports object detection and image classification. Object detection can be enabled by setting the OBJECT_DETECTION_ENABLED environment variable to true. To enable image classification set the IMAGE_CLASSIFICATION environment variable.

⁠Routes

⁠/detect-objects

Use POST /detect-objects to receive JSON-data containing the detected objects, the probability and the position of the detected object in the image. An example call with curl would look like this: curl -F '[email protected]' http://baseurl/detect-objects.

⁠/classify-image

Use POST /classify-image to receive JSON-data containing the predicted classes and the probability image. An example call with curl would look like this: curl -F '[email protected]' http://baseurl/classify-image.

Both endpoints will return HTTP 500 if the image could be processed. If an endpoint is not enabled by settings the corresponding environment variable when running the container it will return HTTP 403.

⁠/health

To check if the service is up you can use /health, which will return HTTP 200, if everything is running.

⁠/status

To receive information about the current status of the service and the enabled functionalities use /status. This will return a JSON looking like this:

{
  "image-classification":
    {"model":"densenet","enabled":true},
  "object-detection":
    {"model":"yolo","enabled":false}
}

⁠Choosing a model

⁠Object Detection

There are three supported models which are RetinaNet, YOLOv3 and TinyYOLOv3. To choose which model will be used to detect objects set the OBJECT_DETECTION_MODEL environment variable to either yolo, tiny_yolo or retina.

⁠Image Classification

There are three supported models which are MobileNetV2, ResNet50, InceptionV3 and DenseNet121. To choose which model will be used to detect objects set the IMAGE_CLASSIFICATION_MODEL environment variable to either mobilenet, resnet, inception or densenet.

⁠Setting up waitress

The service uses gunicornas a server and exposes port 5001. It uses 5 workers with 8 threads each, restarting each worker after 100-150 requests to avoid memory leaks and instability.

⁠Running tests locally

To run the tests locally using drone, execute ./run_tests.sh.

⁠Stats while running the Models on 'testdata'

⁠Object Detection

⁠RetinaNet (Model Size: 145 MB)

Runtime: ~ 5:34 min
Ressources needed: 50.000 Mhz CPU (Peak), 7.000 MB Memory (taken from Nomads container overview)
5M Duration: 20.192628
5M Fill: 1023.04%
5M: 152

⁠YOLOv3 (Model Size: 237 MB)

Runtime: ~ 10:21 min
Ressources needed: 4.800 Mhz CPU (20.000 Peak), 3.000 MB Memory (taken from Nomads container overview)
5M Duration: 54.752632
5M Fill: 1569.58% 5M: 86

⁠TinyYOLOv3 (Model Size: 34 MB)

Runtime: ~ 10:35 min
Ressources needed: 5.000 Mhz CPU (8.000 Peak), 3.300 MB Memory (taken from Nomads container overview)
5M Duration: 55.691765
5M Fill: 1468.03%
5M: 82

⁠Image Classification

⁠MobileNetV2 (Model Size: 4.82 MB)

Runtime: ~ 1:53 min
Ressources needed: 5800 Mhz CPU (One Single Peak), ? MB Memory (No significant usage shown in Nomads container overview)
5M Duration: 0.777142
5M Fill: 41.97%
5M: 162

⁠ResNet50 (Model Size: 98 MB)

Runtime: ~ 2:15 min
Ressources needed: 3622 Mhz CPU (Peak), ? MB Memory (No significant memory usage visible)
5M Duration: 0.671041
5M Fill: 36.24%
5M: 162

⁠InceptionV3 (Model Size: 91.6 MB)

Runtime: ~ 1:44 min
Ressources needed: 8463 Mhz CPU (Peak), ? MB Memory (No significant memory usage visible)
5M Duration: 0.709005
5M Fill: 38.29%
5M: 162

⁠DenseNet121 (Model Size: 31.6 MB)

Runtime: ~ 2:06 min
Ressources needed: 25.000 Mhz CPU (Peak), ? MB Memory (No significant memory usage visible)
5M Duration: 0.714724
5M Fill: 38.60%
5M: 162

⁠Classification + Object Detection

⁠Most resource expensive model each (RetinaNet + DenseNet)

Combined Runtime: ~ 4:51 min
CPU: 52.000 Mhz (Peak) around 20.000 - 30.000 Mhz most of the time
RAM: ? (Probably Nomads monitor is not working correctly)

⁠Least resource expensive model each (YoloV3 + ResNet50)

Combined Runtime: ~ 10:13 min
CPU: 23.000 Mhz (Peak) around 4.800 Mhz most of the time
RAM: ?