Update README.md
Browse files
README.md
CHANGED
@@ -12,11 +12,12 @@ multilinguality:
|
|
12 |
size_categories:
|
13 |
- 1K<n<10K
|
14 |
source_datasets:
|
15 |
-
- extended
|
16 |
task_categories:
|
17 |
- object-detection
|
18 |
task_ids:
|
19 |
- face-detection
|
|
|
20 |
pretty_name: PP4AV
|
21 |
---
|
22 |
|
@@ -69,18 +70,18 @@ English
|
|
69 |
|
70 |
### Data Instances
|
71 |
|
72 |
-
A data point comprises an image and its face annotations.
|
73 |
|
74 |
```
|
75 |
{
|
76 |
-
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, '
|
77 |
'bbox': [
|
78 |
-
[0.230078
|
79 |
-
[0.5017185
|
80 |
-
[0.695078
|
81 |
-
[0.4089065
|
82 |
-
[0.1843745
|
83 |
-
[0.
|
84 |
]
|
85 |
}
|
86 |
}
|
@@ -89,8 +90,10 @@ A data point comprises an image and its face annotations.
|
|
89 |
### Data Fields
|
90 |
|
91 |
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
|
92 |
-
- `
|
93 |
-
- `bbox`: the bounding box of each face (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format)
|
|
|
|
|
94 |
|
95 |
## Dataset Creation
|
96 |
|
|
|
12 |
size_categories:
|
13 |
- 1K<n<10K
|
14 |
source_datasets:
|
15 |
+
- extended
|
16 |
task_categories:
|
17 |
- object-detection
|
18 |
task_ids:
|
19 |
- face-detection
|
20 |
+
- license-plate-detection
|
21 |
pretty_name: PP4AV
|
22 |
---
|
23 |
|
|
|
70 |
|
71 |
### Data Instances
|
72 |
|
73 |
+
A data point comprises an image and its face and license plate annotations.
|
74 |
|
75 |
```
|
76 |
{
|
77 |
+
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'objects': {
|
78 |
'bbox': [
|
79 |
+
[0 0.230078 0.317081 0.239062 0.331367],
|
80 |
+
[1 0.5017185 0.0306425 0.5185935 0.0410975],
|
81 |
+
[1 0.695078 0.0710145 0.7109375 0.0863355],
|
82 |
+
[1 0.4089065 0.31646 0.414375 0.32764],
|
83 |
+
[0 0.1843745 0.403416 0.201093 0.414182],
|
84 |
+
[0 0.7132 0.3393474 0.717922 0.3514285]
|
85 |
]
|
86 |
}
|
87 |
}
|
|
|
90 |
### Data Fields
|
91 |
|
92 |
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
|
93 |
+
- `objects`: a dictionary of face and license plate bounding boxes present on the image
|
94 |
+
- `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in `.txt` file for each `.png` image file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`:
|
95 |
+
- `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object
|
96 |
+
- `x_center`: normalized x-axis coordinate of the center of the bounding box. `x_center = <absolute_x_center> / <image_width>`
|
97 |
|
98 |
## Dataset Creation
|
99 |
|