Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A PyTorch toolkit for 2D Human Pose Estimation.
| Date | Stars |
|---|---|
| 2026-07-24 | 1121 |
| 2026-07-25 | 1121 |
| 2026-07-28 | 1121 |
| 2026-07-30 | 1121 |
| 2026-08-06 | 1121 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# PyTorch-Pose
PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options for the most popular human pose databases (e.g., [the MPII human pose](http://human-pose.mpi-inf.mpg.de), [LSP](http://www.comp.leeds.ac.uk/mat4saj/lsp.html) and [FLIC](http://bensapp.github.io/flic-dataset.html)).
Some codes for data preparation and augmentation are brought from the [Stacked hourglass network](https://github.com/anewell/pose-hg-train). Thanks to the original author.
**Update: this repository is compatible with PyTorch 0.4.1/1.0 now!**
## Features
- Multi-thread data loading
- Multi-GPU training
- Logger
- Training/testing results visualization
## Installation
1. PyTorch (>= 0.4.1): Please follow the [installation instruction of PyTorch](http://pytorch.org/). Note that the code is developed with Python2 and has not been tested with Python3 yet.
2. Clone the repository with submodule
```
git clone --recursive https://github.com/bearpaw/pytorch-pose.git
```
3. Create a symbolic link to the `images` directory of the MPII dataset:
```
ln -s PATH_TO_MPII_IMAGES_DIR data/mpii/images
```
For training/testing on COCO, please refer to [COCO Readme](https://github.com/bearpaw/pytorch-pose/blob/master/data/mscoco/README.md).
<!-- 4. Modify your `.bashrc` file:
```
export PYTHONPATH=".:$PYTHONPATH"
``` -->
4. Download annotation file:
* (MPII) Download [mpii_annotations.json](https://drive.google.com/open?id=1mQrH_yVHeB93rzCfyq5kC9ZYTwZeMsMm) and save it to `data/mpii`
* (MSCOCO) Download [coco_annotations_2014.json](https://drive.google.com/open?id=1jrxis4ujrLlkwoD2GOdv3PGzygpQ04k7) or/and [coco_annotations_2017.json](https://drive.google.com/open?id=1YuzpScAfzemwZqUuZBrbBZdoplXEqUse) and save it to `data/mscoco`
## Usage
**Please refer to [TRAINING.md](TRAINING.md) for detailed training recipes!**
### Testing
You may download our pretrained models (e.g., [2-stack hourglass model](https://drive.google.com/drive/folders/0B63t5HSgY4SQQ2FBRE5rQ2EzbjQ?resourcekey=0-EyrDJs1JYnC5byj5GydbPQ&usp=sharing)) for a quick start.
Run the following command in terminal to evaluate the model on MPII validation split (The train/val split is from [Tompson et al. CVPR 2015](http://www.cims.nyu.edu/~tompson/data/mpii_valid_pred.zip)).
```
CUDA_VISIBLE_DEVICES=0 python example/main.py --dataset mpii -a hg --stacks 2 --blocks 1 --checkpoint checkpoint/mpii/hg_s2_b1 --resume checkpoint/mpii/hg_s2_b1/model_best.pth.tar -e -d
```
* `-a` specifies a network architecture
* `--resume` will load the weight from a specific model
* `-e` stands for evaluation only
* `-d` will visualize the network output. It can be also used during training
The result will be saved as a `.mat` file (`preds_valid.mat`), which is a `2958x16x2` matrix, in the folder specified by `--checkpoint`.
#### Evaluate the [email protected] score
##### Evaluate with MATLAB
You may use the matlab script `evaluation/eval_PCKh.m` to evaluate your predictions. The evaluation code is ported from [Tompson et al. CVPR 2015](http://www.cims.nyu.edu/~tompson/data/mpii_valid_pred.zip).
The results ([email protected] score) trained using this code is reported in the following table.
| Model | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean |
| ---------------- | -----| -------- | ----- | ----- | ---- | ------|------ | ---- |
| hg_s2_b1 (last) | 95.80| 94.57 | 88.12 | 83.31 | 86.24| 80.88 | 77.44 | 86.76|
| hg_s2_b1 (best) | 95.87| 94.68 | 88.27 | 83.64 | 86.29| 81.20 | 77.70 | 86.95|
| hg_s8_b1 (last) | 96.79| 95.19 | 90.08 | 85.32 | 87.48| 84.26 | 80.73 | 88.64|
| hg_s8_b1 (best) | 96.79| 95.28 | 90.27 | 85.56 | 87.57| 84.3 | 81.06 | 88.78|
Training / validation curve is visualized as follows.
##### Excerpt of 5,501 characters
Read on GitHub
Wei Yang · NVIDIA · United States
75
3
2
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cf54777c5a70707c, topic:pose-estimation, desc:pose estimation, readme:pose estimation
matched fp:cf54777c5a70707c, topic:pytorch