Deep features for skin detection and segmentation
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This package contains the deep features extracted from the HGR (http://sun.aei.polsl.pl/~mkawulok/gestures/index.html) and ECU databases presenting hand gestures and human skin in color images, respectively. Additionally, a script (read_binary_file.py) that can be conveniently used for reading these features from the binary files is included in the package. The binary files with deep features are saved in the following form: N_features (4 bytes integer), N_labels (4 bytes integer), H (4 bytes integer), W (4 bytes integer), with H and W being the height and width of an input image. Then, there are H * W rows with N_features * feature_size (4 bytes float), and a label of a size label_size (4 bytes float) - each row represents a single pixel from an input image with the corresponding class label (the 0 label denotes the background, whereas ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["classification","deep feature","deep learning","skin detection","skin segmentation","U-Net"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This package contains the deep features extracted from the HGR (http://sun.aei.polsl.pl/~mkawulok/gestures/index.html) and ECU databases presenting hand gestures and human skin in color images, respectively. Additionally, a script (read_binary_file.py) that can be conveniently used for reading these features from the binary files is included in the package. The binary files with deep features are saved in the following form: N_features (4 bytes integer), N_labels (4 bytes integer), H (4 bytes integer), W (4 bytes integer), with H and W being the height and width of an input image. Then, there are H * W rows with N_features * feature_size (4 bytes float), and a label of a size label_size (4 bytes float) - each row represents a single pixel from an input image with the corresponding class label (the 0 label denotes the background, whereas ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This package contains the deep features extracted from the HGR (http://sun.aei.polsl.pl/~mkawulok/gestures/index.html) and ECU databases presenting hand gestures and human skin in color images, respectively. Additionally, a script (read_binary_file.py) that can be conveniently used for reading these features from the binary files is included in the package. The binary files with deep features are saved in the following form: N_features (4 bytes integer), N_labels (4 bytes integer), H (4 bytes integer), W (4 bytes integer), with H and W being the height and width of an input image. Then, there are H * W rows with N_features * feature_size (4 bytes float), and a label of a size label_size (4 bytes float) - each row represents a single pixel from an input image with the corresponding class label (the 0 label denotes the background, whereas 
classification; deep feature; deep learning; skin detection; skin segmentation; U-Net