Update README.md

README.md

@@ -9,9 +9,9 @@ The SynthPose model was proposed in [OpenCapBench: A Benchmark to Bridge Pose Es
 # Intended use cases
 
 This model uses DarkPose with an HRNet backbone.
-SynthPose is a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data.
-More details are available in [OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics](https://arxiv.org/abs/2406.09788).
-This particular variant was finetuned on a set of keypoints usually found on Motion Capture setups, and include coco keypoints as well.
+SynthPose is a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data.  
+More details are available in [OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics](https://arxiv.org/abs/2406.09788).  
+This particular variant was finetuned on a set of keypoints usually found on motion capture setups, and include coco keypoints as well.
 
 The model predicts the following 52 markers:
 
@@ -120,3 +120,9 @@ result_generator = inferencer("football.mp4", pred_out_dir='predictions', vis_ou
 results = [result for result in result_generator]
 ```
 
+## Training
+
+Finetuning a model using SynthPose can be done by adapting the `td-hm_hrnet-w48_dark-8xb32-210e_merge_bedlam_infinity_coco_3DPW_eval_rich-384x288_pretrained.py` config on the following [MMPose fork](https://github.com/yonigozlan/mmpose).  
+To create annotations on a synthetic dataset (such as BEDLAM) using SynthPose, the tools present in [this repository](https://github.com/yonigozlan/OpenCapBench/tree/main/synthpose) can be used (better documentation to come).
+
+