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Finetuning Sapiens: 2D Pose Estimation

This guide outlines the process to finetune the pretrained Sapiens model for top-down 2D pose estimation.
We use two datasets for training as an example.

  • COCO-WholeBody: 133 keypoints (17 kps body, 6 kps feet, 68 kps face, 42 kps hands).
  • COCO: 17 keypoints

📂 1. Data Preparation

Set $DATA_ROOT as your training data root directory.
Download the images and 17 kps annotations from COCO. Download the 133 kps annotations from COCO-WholeBody. Unzip the images and annotations as subfolders to $DATA_ROOT.
Additionally, download the bounding-box detection on the val2017 set from COCO_val2017_detections_AP_H_70_person.json and place it under $DATA_ROOT/person_detection_results.

The data directory structure is as follows:

  $DATA_ROOT/
  │   └── train2017
  │   │   └── 000000000009.jpg
  │   │   └── 000000000025.jpg
  │   │   └── 000000000030.jpg
  │   └── val2017
  │   │   └── 000000000139.jpg
  │   │   └── 000000000285.jpg
  │   │   └── 000000000632.jpg
  │   └── annotations
  │   │   └── person_keypoints_train2017.json
  │   │   └── person_keypoints_val2017.json
  │   │   └── coco_wholebody_train_v1.0.json
  │   │   └── coco_wholebody_val_v1.0.json
  │   └── person_detection_results
  │   │   └── COCO_val2017_detections_AP_H_70_person.json

⚙️ 2. Configuration Update

Let $DATASET be either coco-wholebody for 133 kps or coco for 17 kps.
Edit $SAPIENS_ROOT/pose/configs/sapiens_pose/$DATASET/sapiens_1b-210e_$DATASET-1024x768.py:

  1. Set pretrained_checkpoint to your checkpoint path.
  2. Update train_dataloader.dataset.data_root and val_dataloader.dataset.data_root to your $DATA_ROOT. eg. data/coco.
  3. Update val_evaluator.ann_file to also point to validation annotation file under $DATA_ROOT.
  4. Update bbox_file to point to the bounding box detection file under $DATA_ROOT.

🏋️ 3. Finetuning

The following guide is for Sapiens-1B. You can find other backbones to finetune under pose_configs_133 and pose_configs_17.
The training scripts are under: $SAPIENS_ROOT/pose/scripts/finetune/$DATASET/sapiens_1b
Make sure you have activated the sapiens python conda environment.

A. 🚀 Single-node Training

Use $SAPIENS_ROOT/pose/scripts/finetune/$DATASET/sapiens_1b/node.sh.

Key variables:

  • DEVICES: GPU IDs (e.g., "0,1,2,3,4,5,6,7")
  • TRAIN_BATCH_SIZE_PER_GPU: Default 2
  • OUTPUT_DIR: Checkpoint and log directory
  • RESUME_FROM: Checkpoint to resume training from. Starts training from previous epoch. Defaults to empty string.
  • LOAD_FROM: Checkpoint to load weight from. Starts training from epoch 0. Defaults to empty string.
  • mode=multi-gpu: Launch multi-gpu training with multiple workers for dataloading.
  • mode=debug: (Optional) To debug. Launched single gpu dry run, with single worker for dataloading. Supports interactive debugging with pdb/ipdb.

Note, if you wish to finetune from an existing pose estimation checkpoint, set the LOAD_FROM variable.

Launch:

cd $SAPIENS_ROOT/pose/scripts/finetune/$DATASET/sapiens_1b
./node.sh

B. 🌐 Multi-node Training (Slurm)

Use $SAPIENS_ROOT/pose/scripts/finetune/$DATASET/sapiens_1b/slurm.sh

Additional variables:

  • CONDA_ENV: Path to conda environment
  • NUM_NODES: Number of nodes (default 4, 8 GPUs per node)

Launch:

cd $SAPIENS_ROOT/pose/scripts/finetune/$DATASET/sapiens_1b
./slurm.sh