Merge branch 'main' of https://github.com/Nunigan/HardwareAcceleratedPoseTracking into main

README.md

@@ -5,17 +5,17 @@ with Xilinx.
 
 ## Project: [Hardware Accelerated Pose Tracking](https://www.hackster.io/michi_michi/hardware-accelerated-pose-tracking-d5ebb9)
 
-- Vitis AI folder
+- Prebuilt
+  - All the files needed to run ICAIPose and the hourglass example on the KV260
+  - prebuilt.tar.xz the same folder compressed for an easy download directly on the KV260
+- Vitis AI
   - All data to compile the neural network with Vitis AI
-  - run the start docker.sh file to get the correct vitisAI version.
-  - with the script "run_all.sh" all the scripts to compile the network are executed.
-- hourglass folder
-  - The hourglass network from VitisAI Model Zoo
-- weights folder
+  - Run the start docker.sh file to get the correct Vitis AI version
+  - With the script "run_all.sh" all the scripts rquired to compile the network are executed
+- Hourglass 
+  - The hourglass network from Vitis AI Model Zoo
+- Weights 
   - The pretrained weights for the network ICAIPose
-- prebuilt folder
-  - all the files needed to run ICAIPose and the hourglass example on the KV260
-  - prebuilt.tar.xz the same folder compressed for an easy download to the KV260
 - run_ICAIPose.py
   - Python script with various utility functions 
         

trained_model/ICAIPose/models/Prelu/ICAIPose_5/config.toml

-
-title = "Config file ICAIPose"
-
-[project]
-name = "PoseEstimationTrain"
-description = "Efficient human pose estimation using modified OpenPose network"
-version = "1.0.0"
-author = "Simon Walser"
-
-[network]
-width_1 = [96, 256] #[96, 256]
-width_2 = [96, 256] #[128,512]
-width_3 = [96, 256] #[96, 256]
-width_4 = [96, 256] #[128,512]
-paf_channels  = 28
-conf_channels = 16
-
-[training]
-img_res = 256
-epochs  = 800
-lr_boundaries  = [     1,     200,     400,     600]      # In epochs -> it is converted to steps afterwards
-lr_values      = [1e-5,  1e-4,    5e-5,    1e-5,    5e-6] # One more than lr_boundaries
-evaluate_every = 541                                      # In steps
-epoch_list     = [200, 400, 600]
-sigma_list     = [13.0, 10.0, 7.0, 5.0]
-loss_factors   = [85.0, 85.0, 30.2, 30.2, 29.7]
-
-[data]
-src_path = '/mnt/data/s1walser/PhysioAI/Data/poseestimationtrain/256'
-dst_path = '/mnt/data/s1walser/PhysioAI/trained_models/poseestimationtrain/256'
-# src_path = '/home/simon/work/work_OST/PhysioAI/Data/poseestimationtrain/256'
-# dst_path = '/home/simon/work/work_OST/PhysioAI/trained_models/poseestimationtrain/256'
-save_attribute = '5'
-batch_size = 32
-ds_size_train = 51958
-ds_size_valid = 2142
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