bozliu / CelebA-Challenge

40 face attributes prediction on CelebA benchmark with PyTorch Implementation.

AI6126 Project 1: CelebA Facial Attribute Recognition Challenge

40 face attributes prediction on CelebA benchmark with PyTorch Implementation.
The challange is to deal with domain gap and imbalanced data of the dataset.
Three effective ways are summarized in my blog article.

Baseline

Face attribute prediction

Dependencies

• Anaconda3 (Python 3.7.6, with Numpy etc.)
• PyTorch 1.6
• tensorboard, tensorboardX

Dataset

• CelebA dataset is a large-scale face dataset with attribute-based annotations. Cropped and aligned face regions are utilized as the training source.
• Pre-processed data and specific split list has been uploaded to list directory.
• lfwA+ dataset is the private test dataset.

Data Pre-processing

After downloading the dataset, please unzip img_align_celeba. Put the folder of Anno with two files, list_eval_partition.txt and list_attr_celeba.txt inside. The folder of img_align_celeba and Anno are in the same directory with Data Pre-processing.py

Dataset Structure

├── Anno
├── identity_CelebA.txt
├── list_attr_celeba.txt
├── list_bbox_celeba.txt
├── list_eval_partition.txt
├── list_landmarks_align_celeba.txt
└── list_landmarks_celeba.txt

├── Eval
└── list_eval_partition.txt

├── img_align_celeba [Face Pictures]
├── test_attr_list.txt
├── train_attr_list.txt
└── val_attr_list.txt

Checkpoints

1. checkpoints_CrossEntropyLoss20 : Training with Cross Entropy loss for 20 epochs
2. checkpoints_FocalLoss20: Training with focal loss for 20 epochs
3. checkpoints+CrossEntropy20+FocalLoss7: Training with Cross Entropy loss for 20 epochs and continue to train with focal loss for another 7 epochs

Run Scripts

Train

! python main.py -d './img_align_celeba' --resume './face-attribute-prediction/checkpoints/checkpoint.pth.tar' --gpu-id '0,1'

-d refers to the location of image dataset, img_align_celeba
--resume refers to the location of checkpoints
--gpu-id refers to the GPU number on the cluster

Validation

! python main.py -d './img_align_celeba' -e --gpu-id '0,1'
-e refers to the validation mode

Test

! python main.py -d './img_align_celeba' -t --gpu-id '0,1'
-t refers to the test mode

Results

• Table1. Average accuracy with the combined loss function, cross-entropy loss for 20 epochs and focal loss for 7 epochs

	Validation Accuracy	Test Accuracy
Average Accuracy	91.3018	90.9309

• Table 2 Average accuracy for single loss function

Single Loss Function	Maximum Training epochs	Validation Accuracy	Test Accuracy
Cross-Entropy	20	90.7780	90.5587
Focal Loss	20	91.1635	90.7420

• Table 3. Accuracy for each attribute with the combined two loss function (cross-entropy loss for 20 epochs and focal loss for 7 epochs)

Attributes	Validation Accuracy	Test Accuracy
5 o’clock Shadow	93.8139	94.2240
Arched Eyebrows	83.9936	82.7322
Attractive	80.5356	82.1210
Bags Under Eyes	83.9332	84.3753
Bald	98.7920	98.8077
Bangs	95.6460	95.5265
Big Lips	82.4986	71.5109
Big Nose	82.4936	83.6389
Black Hair	91.3928	89.7305
Blond Hair	95.3591	95.8271
Blurry	96.3105	96.0876
Brown Hair	85.7603	89.2997
Bushy Eyebrows	92.1679	92.3404
Chubby	95.1981	95.3411
Double Chin	96.2652	96.1427
Eyeglasses	99.3507	99.4590
Goatee	96.2853	97.3299
Gray Hair	97.7551	98.0212
Heavy Makeup	91.8206	91.1332
High cheekbones	87.7133	86.8751
Male	98.1376	97.8609
Mouth Slightly Open	93.4867	93.1770
Mustache	96.1595	96.7588
Narrow Eyes	92.6612	87.0905
No Beard	95.6863	95.6217
Oval Face	75.3914	74.9775
Pale Skin	96.8943	97.1245
Pointy Nose	76.4031	76.4703
Receding Hairline	94.3474	93.3774
Rosy Cheeks	95.0018	95.1007
Sideburns	96.9094	97.6155
Smiling	92.4800	92.2553
Straight Hair	82.8963	82.7071
Wavy Hair	84.5321	83.0578
Wearing Earrings	90.4817	89.5301
Wearing Hat	98.7869	98.9831
Wearing Lipstick	92.2384	93.6279
Wearing Necklace	88.6646	86.7398
Wearing Necktie	96.2249	96.7188
Young	87.6026	87.9170

Detail File Structure

├── celeba.py
├── checkpoints
├── checkpoint.pth.tar
├── log.eps
├── logs
├── log.txt
├── model_best.pth.tar
├── checkpoints_CrossEntropyLoss20
├── checkpoints_FocalLoss20
├── focal_loss.py
├── LICENSE
├── main.py
├── models
├── init.py
├── mobilenetv1.py
├── mobilenetv2.py
└── resnet.py
├── Model Structure
├── Model Summary
├── prediction.txt
├── README.md
└── utils

• celeba.py performs a map-style dataset representing a map from indices/keys to data samples used as one of inputs for DataLoader.
• checkpoints is to save the checkpoint of model in each epoch and also to save the model with highest validation accuracy so far. The loss function of the saved model checkpoints folder are two combined loss function with cross-entropy loss for the first 20 epochs and focal loss for the next 7 epochs
• checkpoints_CrossEntropyLoss20 is to save the checkpoint of model with highest validation accuracy during the training with single loss function, cross-entropy loss for 20 epochs.
• checkpoints_FocalLoss20 is to save the checkpoint of model with highest validation accuracy during the training with single loss function, focal loss for 20 epochs.
• focal_loss.py implements the focal loss function.
• main.py is the main function of the project
• models is the folder with different model structure. resnet.py is the one used in this project.
• Model Structure print the model, ResNet-50 used in this project
• Model Summary prints all layers in ResNet-50, the output shape and parameters of each layers, and the total parameters used for an single image input.
• prediction.txt is predictions of the given new test set
• utils are the utility function required by the project