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[{'expected': SplitInfo(name='train', num_bytes=482079410, num_examples=98, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=266928702, num_examples=50, shard_lengths=None, dataset_name='hair-detection-and-segmentation')}]
Error code:   UnexpectedError

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id
int32
image
image
mask
image
collage
image
shapes
string
0
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"[{'type': 'polygon', 'label': 'hair', 'points': [(375.98, 438.7), (374.93, 480.39), (380.77, 523.37), (391.16, 542.76), (389.99, 518.47), (386.37, 489.27), (382.05, 469.88)]}, {'type': 'tag', 'label': 'is_hair'}]"
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"[{'type': 'polygon', 'label': 'hair', 'points': [(2701.0, 2272.37), (2713.25, 2271.58), (2741.72, 2370.83), (2768.61, 2448.33), (2798.66, 2512.39), (2789.17, 2518.32), (2754.38, 2436.07), (2721.95, 2340.78)]}, {'type': 'polygon', 'label': 'hair', 'points': [(2795.26, 2266.21), (2834.64, 2266.2), (2903.45, 2279.97), (2983.16, 2335.01), (3045.79, 2399.54), (3098.46, 2547.1), (3093.72, 2551.37), (3102.73, 2608.31), (3121.24, 2670.47), (3102.26, 2634.88), (3073.31, 2569.88), (3044.37, 2502.5), (3031.08, 2500.6), (3011.63, 2486.84), (3017.32, 2507.25), (3059.55, 2628.24), (3048.64, 2629.66), (3030.13, 2552.32), (3004.99, 2498.23), (2961.81, 2443.66), (2947.1, 2423.74), (2918.63, 2409.03), (2939.51, 2438.92), (2957.54, 2474.98), (2999.77, 2525.75), (3018.75, 2538.56), (3018.75, 2542.83), (2967.5, 2505.35), (2907.24, 2434.65), (2891.58, 2411.4), (2920.53, 2488.27), (2944.25, 2543.78), (2928.59, 2537.14), (2880.2, 2428.96), (2837.49, 2351.61)]}, {'type': 'polygon', 'label': 'hair', 'points': [(1675.06, 2296.56), (1748.03, 2292.46), (1748.03, 2311.32), (1740.65, 2309.68), (1739.84, 2298.2), (1727.54, 2298.2), (1730.82, 2347.4), (1717.7, 2349.04), (1719.34, 2299.84), (1707.86, 2299.84), (1710.32, 2336.74), (1703.76, 2365.43), (1692.28, 2376.91), (1680.8, 2329.36)]}, {'type': 'polygon', 'label': 'hair', 'points': [(1834.62, 2291.64), (1860.04, 2293.28), (1846.92, 2359.69), (1837.08, 2357.23), (1845.28, 2298.2), (1836.26, 2304.76), (1829.7, 2334.28), (1823.96, 2334.28)]}, {'type': 'polygon', 'label': 'hair', 'points': [(1898.85, 2288.88), (1945.54, 2288.89), (1930.74, 2337.85), (1927.32, 2366.89), (1910.81, 2362.91), (1904.55, 2319.06), (1912.52, 2295.15), (1904.55, 2296.86), (1896.01, 2317.35), (1894.3, 2310.52)]}, {'type': 'polygon', 'label': 'hair', 'points': [(2368.6, 2281.48), (2439.77, 2280.34), (2436.36, 2342.41), (2417.57, 2324.19), (2400.49, 2283.76), (2383.97, 2288.89), (2376.57, 2308.81), (2368.6, 2332.16), (2369.74, 2294.58)]}, {'type': 'polygon', 'label': 'hair', 'points': [(2554.34, 2270.0), (2622.4, 2270.0), (2643.71, 2352.81), (2660.93, 2438.9), (2664.21, 2483.17), (2667.49, 2505.31), (2655.19, 2491.37), (2637.98, 2435.62), (2627.32, 2433.16), (2620.76, 2375.76), (2594.52, 2341.33), (2610.1, 2385.6), (2614.2, 2415.12), (2607.64, 2415.12), (2578.12, 2333.95)]}, {'type': 'polygon', 'label': 'hair', 'points': [(1349.64, 3228.34), (1396.39, 3534.38), (1459.87, 3749.45), (1510.73, 3859.26), (1498.41, 3809.24), (1481.97, 3752.71), (1504.78, 3789.69), (1524.19, 3803.86), (1529.15, 3793.66), (1517.67, 3788.41), (1500.11, 3769.29), (1470.21, 3711.62), (1431.82, 3604.94), (1397.39, 3450.79), (1368.77, 3340.13)]}, {'type': 'tag', 'label': 'is_hair'}]"
47
"[{'type': 'polygon', 'label': 'hair', 'points': [(1922.21, 1626.48), (1980.13, 1644.77), (2010.62, 1705.75), (2045.68, 1838.37), (2047.2, 2077.69), (1996.9, 2277.38), (1940.5, 2493.85), (1920.68, 2364.27), (1922.21, 2251.47), (1952.2, 2143.4), (1957.9, 2091.4), (1957.27, 2041.11), (1966.41, 1999.95), (1969.46, 1928.3), (1974.03, 1788.06), (1931.35, 1681.36)]}, {'type': 'tag', 'label': 'is_hair'}]"
48
"[{'type': 'tag', 'label': 'no_hair'}]"
49
"[{'type': 'polygon', 'label': 'hair', 'points': [(1833.79, 1174.04), (1411.82, 1174.04), (1026.69, 1217.58), (695.14, 1354.89), (531.04, 1502.24), (484.15, 1649.6), (510.94, 1830.44), (601.37, 1676.39), (688.44, 1579.27), (651.6, 1696.48), (644.9, 1743.37), (681.74, 1703.18), (772.16, 1565.87), (822.4, 1562.52), (909.47, 1512.29), (989.85, 1498.89), (1163.99, 1475.45), (1257.77, 1378.33), (1354.89, 1334.79), (1468.75, 1324.75), (1609.41, 1331.44), (1699.83, 1321.4), (1796.95, 1244.37)]}, {'type': 'tag', 'label': 'is_hair'}]"

Hair Detection & Segmentation Dataset

The dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.

The dataset contains a diverse collection of images depicting people with different hair styles, colors, lengths, and textures. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.

The dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.

Get the dataset

This is just an example of the data

Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset.

Dataset structure

  • images - contains of original images of people
  • masks - includes segmentation masks for the original images
  • collages - includes original images with colored hairs within the oval of the face
  • annotations.xml - contains coordinates of the bounding boxes and labels, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.

Tags for the images:

  • is_hair - contains of original images of people
  • no_hair - includes segmentation masks for the original images

Example of XML file structure

Hair Detection & Segmentation might be made in accordance with your requirements.

**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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