https://github.com/apolloauto/apollo-model-yolox
[Apollo] This is the implementation of yolox3d first stage, a camera 2D object detection for autonomous driving perception.
https://github.com/apolloauto/apollo-model-yolox
Last synced: 9 months ago
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[Apollo] This is the implementation of yolox3d first stage, a camera 2D object detection for autonomous driving perception.
- Host: GitHub
- URL: https://github.com/apolloauto/apollo-model-yolox
- Owner: ApolloAuto
- License: apache-2.0
- Created: 2023-11-08T07:07:24.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-07T09:08:36.000Z (over 2 years ago)
- Last Synced: 2025-06-26T13:47:39.810Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 11 MB
- Stars: 10
- Watchers: 10
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
## Introduction
YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities.
For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/2107.08430).
This repo is an implementation of PyTorch version YOLOX, there is also a [MegEngine implementation](https://github.com/MegEngine/YOLOX).
## Updates!!
* 【2023/10/23】 We employ YOLOX(commit id ac58e0a5e68e57454b7b9ac822aced493b553c53) as the first stage in [Apollo](https://github.com/ApolloAuto/apollo) camera_detection_multi_stage component.
* 【2023/02/28】 We support assignment visualization tool, see doc [here](./docs/assignment_visualization.md).
* 【2022/04/14】 We support jit compile op.
* 【2021/08/19】 We optimize the training process with **2x** faster training and **~1%** higher performance! See [notes](docs/updates_note.md) for more details.
* 【2021/08/05】 We release [MegEngine version YOLOX](https://github.com/MegEngine/YOLOX).
* 【2021/07/28】 We fix the fatal error of [memory leak](https://github.com/Megvii-BaseDetection/YOLOX/issues/103)
* 【2021/07/26】 We now support [MegEngine](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/MegEngine) deployment.
* 【2021/07/20】 We have released our technical report on [Arxiv](https://arxiv.org/abs/2107.08430).
## Quick Start
Installation
Step1. Install YOLOX from source.
```shell
# clone code
git clone git@github.com:ApolloAuto/apollo-model-yolox.git
cd apollo-model-yolox
# creat conda env
conda create -n apollo_yolox python=3.8
conda activate apollo_yolox
# install requirements
pip3 install -r requirements.txt
```
Demo
Step1. Download a pretrained model from the benchmark table.
|Model |size | Params
(M) |[Datasets](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d)| [Class](yolox/data/datasets/voc_classes.py)| weights |
| ------ |:---: | :---: | :----: | :----: |:----: |
|[YOLOX-voc-s](./exps/example/yolox_voc/yolox_voc_s.py) |640 | 8.9 | KITTI | 6| [link](https://github.com/ApolloAuto/apollo-model-yolox/releases/download/model/best_kitti_ckpt.pth) |
|[YOLOX-voc-s](./exps/example/yolox_voc/yolox_voc_s.py) |640 | 8.9 | L4 | 8| [link](https://github.com/ApolloAuto/apollo-model-yolox/releases/download/model/best_L4_ckpt.pth) |
Step2. For example, here we use best_L4_ckpt model:
```shell
python tools/demo.py image -n yolox-s -c /path/to/your/best_L4_ckpt.pth --path sample/ --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
```
then you will find result under path `YOLOX_outputs/yolox_s/`.
Reproduce our results on KITTI
Step1. Prepare KITTI dataset
```shell
cd
ln -s /path/to/your/KITTI ./datasets/KITTI
```
Step2. Tools for kitti type datasets
We provide tools for KITTI type datasets which can help to trans it to VOC type : [readme](datasets/README.md)
Step3. change kitti configs
1. change [yolox_voc_s](exps/example/yolox_voc/yolox_voc_s.py)
- class number: 8 to 6
- data_dir=os.path.join(get_yolox_datadir(), "CUSTOMER") to KITTI
2. change [voc_classes.py](yolox/data/datasets/voc_classes.py) to KITTI class.
3. modify [yolox_voc_s.py](exps/example/yolox_voc/yolox_voc_s.py) todo items.
4. modify [voc.py](yolox/data/datasets/voc.py) line 119 change jpg to png
- self._imgpath = os.path.join("%s", "JPEGImages", "%s.jpg") # to png
Step4. Reproduce our results on KITTI:
```shell
python3 tools/train.py -f exps/example/yolox_voc/yolox_voc_s.py -d 1 -b 16
```
or resume
```shell
python3 tools/train.py -f exps/example/yolox_voc/yolox_voc_s.py -d 1 -b 16 -c /path/to/your/latest_ckpt.pth --resume
```
* -d: number of gpu devices
* -b: total batch size, the recommended number for -b is num-gpu * 8
* --fp16: mixed precision training
* --cache: caching imgs into RAM to accelarate training, which need large system RAM.
* -c: checkpoint file path
If you want to visuialize your results, please refer to [Visualization guides](docs/assignment_visualization.md)
Export
We support batch testing for fast evaluation:
```shell
python tools/export_onnx.py --input data -n yolox-s -c YOLOX_outputs/yolox_voc_s/latest_ckpt.pth --output-name yolox.onnx
```
* --input: onnx model input blob name.
* -c: path of model.
* --output-name: the file name of covert model
**Multi Machine Training**
We also support multi-nodes training. Just add the following args:
* --num\_machines: num of your total training nodes
* --machine\_rank: specify the rank of each node
Suppose you want to train YOLOX on 2 machines, and your master machines's IP is 123.123.123.123, use port 12312 and TCP.
On master machine, run
```shell
python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num_machines 2 --machine_rank 0
```
On the second machine, run
```shell
python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num_machines 2 --machine_rank 1
```
**Logging to Weights & Biases**
To log metrics, predictions and model checkpoints to [W&B](https://docs.wandb.ai/guides/integrations/other/yolox) use the command line argument `--logger wandb` and use the prefix "wandb-" to specify arguments for initializing the wandb run.
```shell
python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache] --logger wandb wandb-project
yolox-m
yolox-l
yolox-x
```
An example wandb dashboard is available [here](https://wandb.ai/manan-goel/yolox-nano/runs/3pzfeom0)
**Others**
See more information with the following command:
```shell
python -m yolox.tools.train --help
```
Evaluation
We support batch testing for fast evaluation:
```shell
python -m yolox.tools.eval -n yolox-s -c yolox_s.pth -b 64 --exp_file exps/example/yolox_voc/yolox_voc_s.py -d 8 --conf 0.001 [--fp16] [--fuse]
yolox-m
yolox-l
yolox-x
```
* --fuse: fuse conv and bn
* -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
* -b: total batch size across on all GPUs
To reproduce speed test, we use the following command:
```shell
python -m yolox.tools.eval -n yolox-s -c yolox_s.pth -b 1 --exp_file exps/example/yolox_voc/yolox_voc_s.py -d 1 --conf 0.001 --fp16 --fuse
yolox-m
yolox-l
yolox-x
```
Tutorials
* [Training on custom data](docs/train_custom_data.md)
* [Caching for custom data](docs/cache.md)
* [Manipulating training image size](docs/manipulate_training_image_size.md)
* [Assignment visualization](docs/assignment_visualization.md)
* [Freezing model](docs/freeze_module.md)
## Deployment
1. [MegEngine in C++ and Python](./demo/MegEngine)
2. [ONNX export and an ONNXRuntime](./demo/ONNXRuntime)
3. [TensorRT in C++ and Python](./demo/TensorRT)
4. [ncnn in C++ and Java](./demo/ncnn)
5. [OpenVINO in C++ and Python](./demo/OpenVINO)
6. [Accelerate YOLOX inference with nebullvm in Python](./demo/nebullvm)
## Cite YOLOX
If you use YOLOX in your research, please cite our work by using the following BibTeX entry:
```latex
@article{yolox2021,
title={YOLOX: Exceeding YOLO Series in 2021},
author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2107.08430},
year={2021}
}
```