mkmohangb / vit-pose Goto Github PK

ViTPose is a 2D Human Pose Estimation model based on the Vision transformer architecture. The official repo is [1]. Goal here is to create a version of VIT Pose without the framework code(mmpose/mmcv) for easy understanding/hacking. Only inference is supported.

Download the model weights from [1] - VitPose-B - single task training - classic decoder.

pip install -r requirements.txt

python main.py

Pretraining of the ViT backbone is done using Masked AutoEncoder(MAE) approach. This was validated using ImageNet / COCO / COCO + AIC. Using COCO + AIC showed similar performance(AP/AR) as ImageNet although the size of COCO + AIC is an order less than ImageNet. So less data is required in pre- training if it is similar to the ones that will be used for training downstream tasks.

The sequence of steps is as follows:

Image => preprocess => model => postprocess => keypoints

• calculate center/scale, do affine_transform
  • (x, y, w, h) - bounding box of detected person in the image that is output by an object detector (e.g. YOLO or EfficientDet)
  • center - x + w/2, y + h/2
  • adjust (w,h) based on the image aspect ratio. scale - ((w,h)/200) * padding (200 is used to normalize the scale)
  • Affine transform
• convert to tensor & /255
• normalize the tensor
• tensor shape is [(1, 3, 256, 192)]

• Backbone - Patch Embedding + Pos. Embedding + Encoder blocks
  • patch embedding implemented using a Conv2D layer with the kernel size and stride equal to the patch size(16) and the out channels equal to the embedding dimension (768). Output shape is [(1, 768, 16, 12)]. Flattened & transposed to [(1, 192, 768)]
  • Position embedding is added to the output of patch emdedding.
  • this embedding output is fed to multiple layers of encoder blocks. Output shape [(1, 192, 768)] is same as input shape.
  • output is reshaped back to [(1, 768, 16, 12)]
• Decoder or Head - outputs heatmaps of size (64 x 48) corresponding to the number of key points
  • Encoder output is fed to a decoder which consists of 2 layers of ConvTranspose2D + BN + ReLU ([(1, 256, 64, 48)]) and a final conv1d layer with (1x1) kernel and 17 out channels([(1, 17, 64, 48)]).

• Heatmaps to keypoints
  • For each heatmap, calculate the location of max value
  • add +/-0.25 shift to the locations for higher accuracy
  • scale = scale * 200. Transform back to the image dimensions -> location * scale + center - 0.5 * scale

1. ViTPose
2. ViTPose-Pytorch