Comments (1)
This is actually optional, if you set --single_look
it will only look at the image, not oversample it.
Here is the section of the Levi/Hassner paper which explains the approach:
Prediction. We experimented with two methods of using the network in order to produce age and gender predictions for novel faces:
- Center Crop: Feeding the network with the face image, cropped to 227 × 227 around the face center.
- Over-sampling: We extract five 227 × 227 pixel crop regions, four from the corners of the 256 × 256 face image, and an additional crop region from the center of the face. The network is presented with all five images, along with their horizontal reflections. Its final prediction is taken to be the average prediction value across all these variations.
We have found that small misalignments in the Adience images, caused by the many challenges of these images (occlusions, motion blur, etc.) can have a noticeable impact on the quality of our results. This second, over-sampling method, is designed to compensate for these small misalignments, bypassing the need for improving alignment quality, but rather directly feeding the network with multiple translated
versions of the same face
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Related Issues (20)
- where is the folder /AgeGenderDeepLearning/Folds/tf/age_test_fold_is_1/run-20854 ? HOT 1
- how to use the guess.py with pre-trained model ? HOT 4
- how to change the script if I use MTCNN to detect the face ? HOT 4
- no gender in output HOT 1
- Using the gender detection model on tf 2.0 HOT 2
- Issue Training Gender HOT 2
- Where is the pruning process for the Tensorflow code?
- Running in Realtime
- Adding a licence
- name 'ImageCoder' is not defined HOT 2
- Cannot work on GPU and ask for Requirements
- Gender Classification Confusion Matrix
- batch size issue when freeze the graph
- python train.py HOT 2
- eval.py display accuracy
- test datasets HOT 1
- Custom Face Detector HOT 3
- Gender detection does not work correctly HOT 1
- Output node name to create a frozen graph
- who has the dataset ? HOT 1
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