Platform Template Object Detection

This is the template of object-detection task for ABEJA Platform.

Requirements

Python 3.6.x
[For local] Install ABEJA Platform CLI

Conditions

Transfer learning from VGG16 ImageNet.
Allow only 1 category with multiple labels
Allow up to two dataset
- with two dataset, either of them should be aliased with val.

Parameters

env	type	description
BATCH_SIZE	int	Batch size. Default `32`.
EPOCHS	int	Epoch number. This template applies "Early stopping". Default `50`.
IMG_SIZE	int	Image size. Currently only SSD300 (size=300) is supported!! Automatically resize to this size. Default `300`.
SHUFFLE	bool	Shuffle train dataset. Default `true`.
RANDOM_SEED	int	Random seed. If set, use it for a data shuffling. Default `None`.
MAX_ITEMS	bool	Number of items to use. Default `None` which means use all items
TEST_SIZE	float	Ratio of test dataset. Default `0.4`
LEARNING_RATE	float	Learning rate. Need to be from `0.0` to `1.0`. Default `0.001`.
MOMENTUM	float	Momentum factor. Need to be from `0.0` to `1.0`. Default `0.0`.
WEIGHT_DECAY	float	Weight decay (L2 penalty). Need to be from `0.0` to `1.0`. Default `0.0`.
DAMPENING	float	Dampening for momentum. Need to be from `0.0`. Default `0.0`.
NESTEROV	float	Enables Nesterov momentum. Default `False`.
CONF_THRESHOLD	float	Confidence threshold to filter out bounding boxes with low confidence. Default `0.01`.
TOP_K	int	Number of bounding boxes to be taken. Default `200`.
NMS_THRESHOLD	float	The threshold for IoU to consider bounding boxes as the same. Default `0.45`.
OVERLAP_THRESHOLD	float	The overlap threshold used when matching boxes. Need to be from `0.0` to `1.0`. Default `0.5`
NEG_POS	int	Hard Negative Mining ratio. Default `3`
CONFIDENCE_THRESHOLD	float	Results above this threshold will be returned. This option is valid only for prediction (or inference). Default `0.1`

Preparation

Before running this script, please install ABEJA Platform CLI. You need to setup your configuration at first.

Debug on local

You can train on local with debug-local command.

$ abeja training debug-local \
  --handler train:handler \
  --image abeja-inc/all-cpu:19.04 \
  --organization_id xxxxx \ 
  --datasets train:xxxxx

Train on ABEJA Platform

Prepare `training.yaml` file

name: my-detection-model-from-template
handler: train:handler
image: abeja-inc/all-gpu:19.04
datasets:
  train: "xxx"
  val: "xxx"

Create Job definition

Create job definition on the platform at first.

abeja training create-job-definition

Create Version & Create Job

When you modify your code, run the command to deploy your code on the platform.

abeja training create-version

After deploying it, you can create the job by running the command below.

abeja training create-job -d "my first job"

After creating job, you can check the status by ABEJA Platform Console.

Inference on local

You can do prediction on local with run-local command.

$ abeja model run-local \
  --handler predict:handler \
  --image abeja-inc/all-cpu:19.04 \
  --environment TRAINING_JOB_DATASETS:'{"data":1111111111111}' \
  --environment CONFIDENCE_THRESHOLD:0.9 \
  --input sample.jpg

environment variables for inference

env	type	description
TRAINING_JOB_DATASETS	str	Dataset IDs. e.g `'{"data":1111111111111}'`. The prediction script fetches the label information from training data.
CONFIDENCE_THRESHOLD	float	Results above this threshold will be returned. This option is valid only for prediction (or inference). Default `0.1`

You can see more detail of the parameters on parameters.py.

Also you can start inference API on local with run-local-server command.

$ abeja model run-local-server \
  --handler predict:handler \
  --image abeja-inc/all-cpu:19.04 \
  --environment TRAINING_JOB_DATASETS:'{"data":1111111111111}' \
  --environment CONFIDENCE_THRESHOLD:0.3 \

Prepare test data

You can use this sample script to test it with Pascal VOC Dataset. See the detail of this repository.

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