This document describes algorithms provided by NHN Cloud AI EasyMaker. By using the underlying algorithms, you can create a machine learning model by preparing a data set without writing any training code.
It is an algorithm (ResNet-50) that classifies types of images.
| Hyperparameter Name | Required | Value Type | Default Value | Range | Description |
|---|---|---|---|---|---|
| input_size | False | int | 28 | [4~∞) | Resolution of the output image |
| learning_rate | False | float | 0.1 | [0.0~∞) | The initial learning rate value of the AdamW optimizer |
| per_device_train_batch_size | False | int | 16 | [2~∞) | Training batch size per GPU/TPU core/CPU |
| per_device_eval_batch_size | False | int | 16 | [1~∞) | evaluation batch size per GPU/TPU core/CPU |
| num_train_epochs | False | int | 3 | [1~∞) | The total number of times the entire training is performed |
| logging_steps | False | int | 500 | [500~∞) | Step cycle to output logs |
Prepare train, validation, and test data sets.
A data set for training. Data sets should be prepared in a directory structure defined as follows.
folder/train/{lable}/image_file.png
Creates a label ({label}) directory for image types, and stores image files in subdirectories.
[Example] Cat-Dog classification train data set
folder/train/cat/bengal.png
folder/train/cat/main_coon.png
folder/train/dog/chihuahua.png
folder/train/dog/golden_retriever.png
...
This is the data set for validation. Data sets should be prepared in a directory structure defined as follows.
folder/validation/{lable}/image_file.png
Creates a label ({label}) directory for image types, and stores image files in subdirectories.
[Example] Cat-Dog classification validation data set
folder/validation/cat/abyssinian.png
folder/validation/cat/aegean.png
folder/validation/dog/billy.png
folder/validation/dog/calupoh.png
...
This is the data set for testing. Data sets should be prepared in a directory structure defined as follows.
folder/test/{lable}/image_file.png
Creates a label ({label}) directory for image types, and stores image files in subdirectories.
[Example] Cat-Dog classification test data set
folder/test/cat/arabian_mau.png
folder/test/cat/american_curl.png
folder/test/dog/boerboel.png
folder/test/dog/cretan_hound.png
...
The Image Classification algorithm produces the following metrics. Indicators generated during training can be checked through Training > Go to TensorBoard.
| Indicator name | Description |
|---|---|
| Accuracy | Number of data correctly predicted by the model / Number of actual data |
| Precision | Average for each class (the number of data correctly predicted by the model / the number of data in the actual corresponding class) |
| Recall | Average for each class (number of data correctly predicted by model/number of data predicted by model for that class) |
| F1-Score | Harmonic Average of Precision and Recall |
To create an endpoint with a trained model and request inference, see Create Endpoint and Request Inference.
The score value for each image type (label) is answered.
[Example] Inference API response body of Cat-Dog classification
[
{
"score": 0.9992493987083435,
"label": "dog"
},
{
"score": 0.0007505337707698345,
"label": "cat"
}
]
An algorithm (SegFormer-B3) that predicts the label of every pixel region within an image.
| Hyperparameter Name | Required | Value Type | Default Value | Valid range | Description |
|---|---|---|---|---|---|
| learning_rate | False | float | 2e-4 | [0.0~∞) | The initial learning rate value of the AdamW optimizer |
| per_device_train_batch_size | False | int | 4 | [0~∞) | Training batch size per GPU/TPU core/CPU |
| num_train_epochs | False | float | 3.0 | [0.0~∞) | The total number of times the entire training is performed |
| logging_steps | False | int | 500 | [500~∞) | Step cycle to output logs |
Prepare train, validation, resources, and test data sets.
A data set for training. Datasets should be prepared in a defined directory structure like this:
folder/train/train.json
folder/train/images/0001.png
folder/train/images/0002.png
folder/train/images/0003.png
...
folder/train/annotations/0001.png
folder/train/annotations/0002.png
folder/train/annotations/0003.png
...
[
{
"image": "images/0001.png",
"seg_map": "annotations/0001.png"
},
{
"image": "images/0002.png",
"seg_map": "annotations/0002.png"
},
{
"image": "images/0003.png",
"seg_map": "annotations/0003.png"
}
]
This is the data set for validation. Datasets should be prepared in a defined directory structure like this:
folder/validation/validation.json
folder/validation/images/0001.png
folder/validation/images/0002.png
folder/validation/images/0003.png
...
folder/validation/annotations/0001.png
folder/validation/annotations/0002.png
folder/validation/annotations/0003.png
...
[
{
"image": "images/0001.png",
"seg_map": "annotations/0001.png"
},
{
"image": "images/0002.png",
"seg_map": "annotations/0002.png"
},
{
"image": "images/0003.png",
"seg_map": "annotations/0003.png"
}
]
Create a dictionary in key-value format to map label IDs to label classes required when setting up the model.
folder/resources/id2lable.json
{
"0": "unlabeled",
"1": "flat-road",
"2": "flat-sidewalk",
"3": "flat-crosswalk",
"...": "..."
}
This is the data set for testing. Datasets should be prepared in a defined directory structure like this:
folder/test/train.json
folder/test/images/0001.png
folder/test/images/0002.png
folder/test/images/0003.png
...
folder/test/annotations/0001.png
folder/test/annotations/0002.png
folder/test/annotations/0003.png
...
[
{
"image": "images/0001.png",
"seg_map": "annotations/0001.png"
},
{
"image": "images/0002.png",
"seg_map": "annotations/0002.png"
},
{
"image": "images/0003.png",
"seg_map": "annotations/0003.png"
}
]
The Semantic Segmentation algorithm generates the following metrics.
Indicators generated during training can be checked through Training > Go to TensorBoard.
| Indicator name | Description |
|---|---|
| mean_iou | The class average of the percentage of overlap between the area predicted by the model and the correct area |
| mean_accuracy | The class mean of the proportion of correct answers equal to the value predicted by the model |
| overall_accuracy | Average of all images with the same proportion of correct answers as the value predicted by the model |
| per_category_accuracy | Percentage of correct answers equal to the value predicted by the model for each class |
| per_category_iou | The overlapping ratio between the area predicted by the model for each class and the correct area |
To create an endpoint with a trained model and request inference, see Create Endpoint and Request Inference.
After resizing the requested image to 512 X 512, the label value for each pixel of each image is returned in the form of an array.
{
"predictions": [
[
[
1, 1, 27, 27, ...
],
[
27, 27, 1, 11, ...
]
...
]
]
}
An algorithm (detr-resnet-50) that predicts the position (bbox) and class (class) of all objects present in an image.
| Hyperparameter Name | Required | Value Type | Default Value | Valid range | Description |
|---|---|---|---|---|---|
| learning_rate | False | float | 2e-4 | [0.0~∞) | The initial learning rate value of the AdamW optimizer |
| per_device_train_batch_size | False | int | 4 | [1~∞) | Training batch size per GPU/TPU core/CPU |
| per_device_eval_batch_size | False | int | 4 | [1~∞) | evaluation batch size per GPU/TPU core/CPU |
| num_train_epochs | False | float | 3.0 | [0.0~∞) | The total number of times the entire training is performed |
| threshold | False | float | 0.5 | [0.0~1.0] | Inference Threshold |
| logging_steps | False | int | 500 | [500~∞) | Step cycle to output logs |
Prepare the train and test data sets.
A data set for training. Datasets should be prepared in a defined directory structure like this:
folder/train/_annotations.coco.json
folder/train/0001.png
folder/train/0002.png
folder/train/0003.png
...
[Example] Example of Balloon Object Detection
{
"info": {
"year": "2022",
"version": "1",
"description": "Exported from roboflow.ai",
"contributor": "",
"url": "https://public.roboflow.ai/object-detection/undefined",
"date_created": "2022-08-23T09:36:56+00:00"
},
"licenses": [
{
"id": 1,
"url": "https://creativecommons.org/licenses/by/4.0/",
"name": "CC BY 4.0"
}
],
"categories": [
{
"id": 0,
"name": "none",
"supercategory": "none"
},
{
"id": 1,
"name": "balloon",
"supercategory": "balloon"
}
],
"images": [
{
"id": 0,
"license": 1,
"file_name": "0001.png",
"height": 416,
"width": 416,
"date_captured": "2022-08-23T09:36:56+00:00"
},
{
"id": 1,
"license": 1,
"file_name": "0002.png",
"height": 416,
"width": 416,
"date_captured": "2022-08-23T09:36:56+00:00"
},
{
"id": 2,
"license": 1,
"file_name": "0003.png",
"height": 416,
"width": 416,
"date_captured": "2022-08-23T09:36:56+00:00"
}
],
"annotations": [
{
"id": 0,
"image_id": 0,
"category_id": 1,
"bbox": [
201,
166,
93.5,
144.5
],
"area": 13510.75,
"segmentation": [],
"iscrowd": 0
},
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [
17,
20,
217.5,
329
],
"area": 71557.5,
"segmentation": [],
"iscrowd": 0
},
{
"id": 2,
"image_id": 2,
"category_id": 1,
"bbox": [
26,
248,
162.5,
117
],
"area": 19012.5,
"segmentation": [],
"iscrowd": 0
}
]
}
This is the data set for validation. Data sets should be prepared in a directory structure defined as follows.
folder/validation/_annotations.coco.json
folder/validation/0001.png
folder/validation/0002.png
folder/validation/0003.png
...
This is the data set for test. Datasets should be prepared in a defined directory structure like this:
folder/test/_annotations.coco.json
folder/test/0001.png
folder/test/0002.png
folder/test/0003.png
...
To create an endpoint with a trained model and request inference, see Create Endpoint and Request Inference.
Returns a list of bboxes (xmin, ymin, xmax, ymax) of detected objects.
{
"predictions": [
[
{
"balloon": {
"xmin": 293,
"ymin": 325,
"xmax": 361,
"ymax": 375
}
},
{
"balloon": {
"xmin": 322,
"ymin": 157,
"xmax": 404,
"ymax": 273
}
}
]
]
}
Please refer to the following guide to create an endpoint and perform inference with a model that has been trained. 1. Select a completed training. 2. After clicking the Create Model button, write a model name and click the Create Model button to create a model. 3. (2) Click Create Endpoint with the model created in . After entering the endpoint setup information, create the endpoint. 4. Click the created endpoint name and select a stage. 5. You can request the real-time inference API through the stage endpoint URL.
{
"instances": [
{
"data": "image_to_bytes_array"
}
]
}
import base64
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="converts image to bytes array",
type=str)
args = parser.parse_args()
image = open(args.filename, 'rb') # open binary file in read mode
image_read = image.read()
image_64_encode = base64.b64encode(image_read)
bytes_array = image_64_encode.decode('utf-8')
request = {
"instances": [
{
"data": bytes_array
}
]
}
with open('input.json', 'w') as outfile:
json.dump(request, outfile, indent=4, sort_keys=True)