IdenProf is a dataset containing images of identifiable professionals.
IdenProf is a dataset of identifiable professionals, collected in order to ensure that machine learning systems can be trained to recognize professionals by their mode of dressing as humans can observe. This is part of our mission to train machine learning systems to perceive, understand and act accordingly in any environment they are deployed.
This is the first release of the IdenProf dataset. It contains 11,000 images that span cover 10 categories of professions. The professions
included in this release are:
- Chef
- Doctor
- Engineer
- Farmer
- Firefighter
- Judge
- Mechanic
- Pilot
- Police
- Waiter
There are 1,100 images for each category, with 900 images for trainings and 200 images for testing . We are working on adding more
categories in the future and will continue to improve the dataset.
>>> DOWNLOAD, TRAINING AND PREDICTION:
The IdenProf dataset is provided for download in the release section of this repository.
You can download the dataset via this link .
We have also provided a python codebase to download the images, train ResNet50 on the images
and perform prediction using a pretrained model (also using ResNet50) provided in the release section of this repository.
The python codebase is contained in the idenprof.py file and the model class labels for prediction is also provided the
idenprof_model_class.json. The pretrained ResNet50 model is available for download via this
link. This pre-trained model was trained over 61 epochs only, but it achieved 79% accuracy on 2000 test images. You can see the prediction results on new images that were not part of the dataset in the Prediction Results section below. More experiments will enhance the accuracy of the model.
Running the experiment or prediction requires that you have Tensorflow, Numpy and Keras installed.
>>> DATASHEET FOR IDENPROF
For transparency and accountability on the collection and content of the IdenProf dataset, we have provided a comprehensive
datasheet on the dataset . The datasheet is based on the blueprint provided in the 2018 paper publication , "Datasheets for Datasets" by Timnit. et al.
The datasheet is available via this link.
>>> Prediction Results
chef : 99.90828037261963 waiter : 0.0905417778994888 doctor : 0.0011116820132883731
firefighter : 80.1691472530365 police : 19.79282945394516 engineer : 0.03719799569807947
farmer : 99.93320107460022 police : 0.06526767974719405 firefighter : 0.0014684919733554125
doctor : 99.70111846923828 chef : 0.2974770264700055 waiter : 0.001407588024449069
waiter : 99.99997615814209 chef : 1.568847380895022e-05 judge : 1.0255866556008186e-05
pilot : 99.75990653038025 mechanic : 0.21259593777358532 police : 0.024273521557915956
farmer : 100.0 waiter : 1.6071012576279742e-09 police : 1.273151375991155e-09
doctor : 95.55137157440186 engineer : 3.5533107817173004 mechanic : 0.6231860723346472
waiter : 99.92395639419556 chef : 0.05305781960487366 judge : 0.01294929679716006
police : 96.9819724559784 pilot : 2.988756448030472 engineer : 0.029250176157802343
engineer : 100.0 pilot : 8.049450689329163e-09 farmer : 1.503418743664664e-09
- T. Gebru et al, Datasheets for Datasets,
https://arxiv.org/abs/1803.09010 - Kaiming H. et al, Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385