https://github.com/kentaroy47/training-domain-specific-models
Framework for training efficient domain specific object detection models in Pytorch
https://github.com/kentaroy47/training-domain-specific-models
deep-learning deep-neural-networks domain-specific-models faster-rcnn object-detection pytorch
Last synced: 4 months ago
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Framework for training efficient domain specific object detection models in Pytorch
- Host: GitHub
- URL: https://github.com/kentaroy47/training-domain-specific-models
- Owner: kentaroy47
- License: mit
- Created: 2018-11-01T21:11:46.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-29T11:30:15.000Z (over 5 years ago)
- Last Synced: 2025-01-20T23:52:38.374Z (4 months ago)
- Topics: deep-learning, deep-neural-networks, domain-specific-models, faster-rcnn, object-detection, pytorch
- Language: Python
- Homepage:
- Size: 9.82 MB
- Stars: 5
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# training-domain-specific-models
[Training domain specific models for efficient object detection arXivpaper](https://arxiv.org/abs/1811.02689)
This is a framework to train domain specific model, which is **accurate + computation efficient!**
Faster-RCNN implementation is based on faster-rcnn.pytorch by jwyang. Thanks!
I strongly recommend to take a look at their readme if you get stuck on frcnn codes.
https://github.com/jwyang/faster-rcnn.pytorch
# What is a domain specific model?
For a backbone of object detection, Resnet101 is a very good model but TOO BIG!
But a small model like Resnet18 has a low accuracy, due to small network capacity.
However, do we need such a big model to do object detection in a limited domain? (Like your office or a particular intersection)
Since backgrounds does not change, even small model should do very well if trained properly!
A domain specific model(DSM) is a model focusing on achieving high accuracy
at such limited domain (e.g. fixed view of an intersection). We argue that DSMs
can capture essential features well even with a small model size.
In this repo, we train a small domain specific model (say res18) in with a dataset of a limited domain.
We see that by training, small models can achieve very high accuracy!
Take a look at a [Youtube Video Demo!](https://youtu.be/h2raGGDunw4)
By domain specific training, the mAP improves ~20%.
# Preparation
## Requirements.
Pytorch 0.4.0
Python 3.x
CUDA 8.0 or higher
## Clone repo
Lets start off by cloning this repo.
```
git clone https://github.com/kentaroy47/training-domain-specific-models.git
cd training-domain-specific-models
```
You may need to compile the rpn scripts.
Please see jwyang's repo for details.
https://github.com/jwyang/faster-rcnn.pytorch
## Download models
We need to prepare Resnet101 and Resnet18 Faster-RCNN model.
```
cd training-domain-specific-models
Download..
wget https://www.dropbox.com/s/ew47jhdu67bdocf/files.tar.gz
tar -zxvf files.tar.gz
```
## Setup dataset
If the models and the video are set, we can prepare the dataset.
1. Res101 model generates the teacher labels.
2. The dataset is prepared in a PASCAL_VOC format for training.
This is done in a single script.
Just run:
```
# for dataset coral
python make_dataset.py --dataset coral
# for dataset jackson2
python make_dataset.py --dataset jackson2
```
### shortcut..
We prepared a dataset.tar in the link bellow, if you want to take a short cut.
Actually cloning the repo will get you the pickle lable files (output/baseline/)
# Training Domain Specific Models!
Run..
This will take about 2 hours on TitanXp.
```
python trainval_net_ds.py --cuda --r True --dataset pascal_voc_jackson2
# or for coral,
python trainval_net_ds.py --cuda --r True --dataset pascal_voc_coral
```
# Evaluation!
We evaluate the accuracy (mAP) with validation images.
The res101 outputs are utilized as ground truth here, since labeling them are cubersome.
```
python demo-and-eval-save.py --net res18 --dataset pascal_voc_jackson2 --cuda --checksession 1 --checkepoch 20 --checkpoint 1 --image_dir images/jackson2_val/ --truth output/baseline/jackson2val-res101.pkl
```
## Ground truths
We also plan to release the hand-labeled ground truth as well.
Interestingly, domain specific model outperforms the accuracy that of res101.
