image_id
int32
0
41
| image
image
| annotations
string
|
|---|---|---|
0 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
10 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
16 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
17 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
18 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
19 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
21 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
26 |  | "[{'type': 'tag', 'label': 'type_6'}]" |
3 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
7 |  | "[{'type': 'tag', 'label': 'type_6'}]" |
9 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
14 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
24 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
25 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
5 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
8 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
12 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
15 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
22 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
23 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
27 |  | "[{'type': 'tag', 'label': 'type_2'}]" |
4 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
6 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
11 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
13 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
20 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
2 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
1 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
28 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
29 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
30 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
31 |  | "[{'type': 'tag', 'label': 'type_3'}]" |
32 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
33 |  | "[{'type': 'tag', 'label': 'type_6'}]" |
34 |  | "[{'type': 'tag', 'label': 'type_6'}]" |
35 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
36 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
37 |  | "[{'type': 'tag', 'label': 'type_6'}]" |
39 |  | "[{'type': 'tag', 'label': 'type_5'}]" |
40 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
38 |  | "[{'type': 'tag', 'label': 'type_7'}]" |
41 |  | "[{'type': 'tag', 'label': 'type_4'}]" |
Dataset of bald people
Dataset consists of 5000 photos of people with 7 stages of hairloss according to the Norwood scale. Dataset is useful for training neural networks for the recommendation systems, optimizing the work processes of trichologists and applications in the Med / Beauty spheres.
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Image
Similar images are presented in the dataset:
Hamilton–Norwood scale
- type_1: There is a lack of bilateral recessions along the anterior border of the hairline in the frontoparietal regions. No notable hair loss or recession of the hairline.
- type_2: There is a small recession of the hairline around the temples. Hair is also lost, or sparse, along the midfrontal border of the scalp, but the depth of the affected area is much less than in the frontoparietal regions. This is commonly referred to as an adult or mature hairline.
- type_3: The first signs of significant balding appear. There is a deep, symmetrical recession at the temples that are only sparsely covered by hair.
- type_4: The hairline recession is harsher than in stage 2, and there is scattered hair or no hair on the vertex. There are deep frontotemporal recessions, usually symmetrical, and are either bare or very sparsely covered by hair.
- type_5: The areas of hair loss are more significant than in stage 4. They are still divided, but the band of hair between them is thinner and sparser.
- type_6: The connection of hair that crosses the crown is gone with only sparse hair remaining. The frontotemporal and vertex regions are joined together, and the extent of hair loss is more significant.
- type_7: The most drastic stage of hair loss, only a band of hair, going around the sides of the head persists. This hair usually is not thick and might be dainty.
Data Format
Each image from img folder is accompanied by an XML-annotation in the annotations.xml file indicating the Hamilton–Norwood type of hairloss for each person in the dataset.
Example of XML file structure
**TrainingData**
More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
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