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https://github.com/kurianbenoy/action-detection_ssd_pytorch

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Host: GitHub
URL: https://github.com/kurianbenoy/action-detection_ssd_pytorch
Owner: kurianbenoy
License: mit
Created: 2019-06-28T10:50:11.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2022-12-08T05:49:38.000Z (almost 2 years ago)
Last Synced: 2024-10-15T03:06:31.494Z (22 days ago)
Language: Python
Size: 72.3 KB
Stars: 0
Watchers: 4
Forks: 0
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Single Shot MultiBox Detector Implementation in Pytorch

This repo implements [SSD (Single Shot MultiBox Detector)](https://arxiv.org/abs/1512.02325). The implementation is heavily influenced by the projects [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) and [Detectron](https://github.com/facebookresearch/Detectron).
The design goal is modularity and extensibility.

Currently, it has MobileNetV1, MobileNetV2, and VGG based SSD/SSD-Lite implementations.

It also has out-of-box support for retraining on Google Open Images dataset.

![Example of Mobile SSD](readme_ssd_example.jpg "Example of Mobile SSD(Courtesy of https://www.pexels.com/@mirit-assaf-299757 for the image.")

## Dependencies
1. Python 3.6+
2. OpenCV
3. Pytorch 1.0 or Pytorch 0.4+
4. Caffe2
5. Pandas
6. Boto3 if you want to train models on the Google OpenImages Dataset.

## Run the demo
### Run the live MobilenetV1 SSD demo

```bash
wget -P models https://storage.googleapis.com/models-hao/mobilenet-v1-ssd-mp-0_675.pth
wget -P models https://storage.googleapis.com/models-hao/voc-model-labels.txt
python run_ssd_live_demo.py mb1-ssd models/mobilenet-v1-ssd-mp-0_675.pth models/voc-model-labels.txt
```
### Run the live demo in Caffe2

```bash
wget -P models https://storage.googleapis.com/models-hao/mobilenet_v1_ssd_caffe2/mobilenet-v1-ssd_init_net.pb
wget -P models https://storage.googleapis.com/models-hao/mobilenet_v1_ssd_caffe2/mobilenet-v1-ssd_predict_net.pb
python run_ssd_live_caffe2.py models/mobilenet-v1-ssd_init_net.pb models/mobilenet-v1-ssd_predict_net.pb models/voc-model-labels.txt
```

You can see a decent speed boost by using Caffe2.

### Run the live MobileNetV2 SSD Lite demo

```bash
wget -P models https://storage.googleapis.com/models-hao/mb2-ssd-lite-mp-0_686.pth
wget -P models https://storage.googleapis.com/models-hao/voc-model-labels.txt
python run_ssd_live_demo.py mb2-ssd-lite models/mb2-ssd-lite-mp-0_686.pth models/voc-model-labels.txt
```

The above MobileNetV2 SSD-Lite model is not ONNX-Compatible, as it uses Relu6 which is not supported by ONNX.
The code supports the ONNX-Compatible version. Once I have trained a good enough MobileNetV2 model with Relu, I will upload
the corresponding Pytorch and Caffe2 models.

You may notice MobileNetV2 SSD/SSD-Lite is slower than MobileNetV1 SSD/Lite on PC. However, MobileNetV2 is faster on mobile devices.

## Pretrained Models

### Mobilenet V1 SSD

URL: https://storage.googleapis.com/models-hao/mobilenet-v1-ssd-mp-0_675.pth

```
Average Precision Per-class:
aeroplane: 0.6742489426027927
bicycle: 0.7913672875238116
bird: 0.612096015101108
boat: 0.5616407126931772
bottle: 0.3471259064860268
bus: 0.7742298893362103
car: 0.7284171192326804
cat: 0.8360675520354323
chair: 0.5142295855384792
cow: 0.6244090341627014
diningtable: 0.7060035669312754
dog: 0.7849252606216821
horse: 0.8202146617282785
motorbike: 0.793578272243471
person: 0.7042670984734087
pottedplant: 0.40257147509774405
sheep: 0.6071252282334352
sofa: 0.7549120254763918
train: 0.8270992920206008
tvmonitor: 0.6459903029666852

Average Precision Across All Classes:0.6755
```

### MobileNetV2 SSD-Lite

URL: https://storage.googleapis.com/models-hao/mb2-ssd-lite-mp-0_686.pth

```
Average Precision Per-class:
aeroplane: 0.6973327307871002
bicycle: 0.7823755921687233
bird: 0.6342429230125619
boat: 0.5478160937380846
bottle: 0.3564069147093762
bus: 0.7882037885117419
car: 0.7444122242934775
cat: 0.8198865557991936
chair: 0.5378973422880109
cow: 0.6186076149254742
diningtable: 0.7369559500950861
dog: 0.7848265495754562
horse: 0.8222948787839229
motorbike: 0.8057808854619948
person: 0.7176976451996411
pottedplant: 0.42802932547480066
sheep: 0.6259124005994047
sofa: 0.7840368059271103
train: 0.8331588002612781
tvmonitor: 0.6555051795079904
Average Precision Across All Classes:0.6860690100560214
```

The code to re-produce the model:

```bash
wget -P models https://storage.googleapis.com/models-hao/mb2-imagenet-71_8.pth
python train_ssd.py --dataset_type voc --datasets ~/data/VOC0712/VOC2007 ~/data/VOC0712/VOC2012 --validation_dataset ~/data/VOC0712/test/VOC2007/ --net mb2-ssd-lite --base_net models/mb2-imagenet-71_8.pth --scheduler cosine --lr 0.01 --t_max 200 --validation_epochs 5 --num_epochs 20
```

### VGG SSD

URL: https://storage.googleapis.com/models-hao/vgg16-ssd-mp-0_7726.pth

```
Average Precision Per-class:
aeroplane: 0.7957406334737802
bicycle: 0.8305351156180996
bird: 0.7570969203281721
boat: 0.7043869846367731
bottle: 0.5151666571756393
bus: 0.8375121237865507
car: 0.8581508869699901
cat: 0.8696185705648963
chair: 0.6165431194526735
cow: 0.8066422244852381
diningtable: 0.7629391213959706
dog: 0.8444541531856452
horse: 0.8691922094815812
motorbike: 0.8496564646906418
person: 0.793785185549561
pottedplant: 0.5233462463152305
sheep: 0.7786762429478917
sofa: 0.8024887701948746
train: 0.8713861172265407
tvmonitor: 0.7650514925384194
Average Precision Across All Classes:0.7726184620009084
```

The code to re-produce the model:

```bash
wget -P models https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
python train_ssd.py --datasets ~/data/VOC0712/VOC2007/ ~/data/VOC0712/VOC2012/ --validation_dataset ~/data/VOC0712/test/VOC2007/ --net vgg16-ssd --base_net models/vgg16_reducedfc.pth --batch_size 24 --num_epochs 200 --scheduler "multi-step” —-milestones “120,160”
```
## Training

```bash
wget -P models https://storage.googleapis.com/models-hao/mobilenet_v1_with_relu_69_5.pth
python train_ssd.py --datasets ~/data/VOC0712/VOC2007/ ~/data/VOC0712/VOC2012/ --validation_dataset ~/data/VOC0712/test/VOC2007/ --net mb1-ssd --base_net models/mobilenet_v1_with_relu_69_5.pth --batch_size 24 --num_epochs 200 --scheduler cosine --lr 0.01 --t_max 200
```

The dataset path is the parent directory of the folders: Annotations, ImageSets, JPEGImages, SegmentationClass and SegmentationObject. You can use multiple datasets to train.

## Evaluation

```bash
python eval_ssd.py --net mb1-ssd --dataset ~/data/VOC0712/test/VOC2007/ --trained_model mobilenet-v1-ssd models/mobilenet-v1-ssd-mp-0_675.pth --label_file models/voc-model-labels.txt
```

## Convert models to ONNX and Caffe2 models

```bash
python convert_to_caffe2_models.py mb1-ssd models/mobilenet-v1-ssd-mp-0_675.pth models/voc-model-labels.txt
```

The converted models are models/mobilenet-v1-ssd.onnx, models/mobilenet-v1-ssd_init_net.pb and models/mobilenet-v1-ssd_predict_net.pb. The models in the format of pbtxt are also saved for reference.

## Retrain on Open Images Dataset

Let's we are building a model to detect guns for security purpose.

Before you start you can try the demo.

```bash
wget -P models https://storage.googleapis.com/models-hao/gun_model_2.21.pth
wget -P models https://storage.googleapis.com/models-hao/open-images-model-labels.txt
python run_ssd_example.py mb1-ssd models/gun_model_2.21.pth models/open-images-model-labels.txt ~/Downloads/big.JPG
```

![Example of Gun Detection](gun.jpg)

If you manage to get more annotated data, the accuracy could become much higher.

### Download data

```bash
python open_images_downloader.py --root ~/data/open_images --class_names "Handgun,Shotgun" --num_workers 20
```

It will download data into the folder ~/data/open_images.

The content of the data directory looks as follows.

```
class-descriptions-boxable.csv test validation
sub-test-annotations-bbox.csv test-annotations-bbox.csv validation-annotations-bbox.csv
sub-train-annotations-bbox.csv train
sub-validation-annotations-bbox.csv train-annotations-bbox.csv
```

The folders train, test, validation contain the images. The files like sub-train-annotations-bbox.csv
is the annotation file.

### Retrain

```bash
python train_ssd.py --dataset_type open_images --datasets ~/data/open_images --net mb1-ssd --pretrained_ssd models/mobilenet-v1-ssd-mp-0_675.pth --scheduler cosine --lr 0.01 --t_max 100 --validation_epochs 5 --num_epochs 100 --base_net_lr 0.001 --batch_size 5
```

You can freeze the base net, or all the layers except the prediction heads.

```
--freeze_base_net Freeze base net layers.
--freeze_net Freeze all the layers except the prediction head.
```

You can also use different learning rates
for the base net, the extra layers and the prediction heads.

```
--lr LR, --learning-rate LR
--base_net_lr BASE_NET_LR
initial learning rate for base net.
--extra_layers_lr EXTRA_LAYERS_LR
```

As subsets of open images data can be very unbalanced, it also provides
a handy option to roughly balance the data.

```
--balance_data Balance training data by down-sampling more frequent
labels.
```

### Test on image

```bash
python run_ssd_example.py mb1-ssd models/mobilenet-v1-ssd-Epoch-99-Loss-2.2184619531035423.pth models/open-images-model-labels.txt ~/Downloads/gun.JPG
```

## ONNX Friendly VGG16 SSD

! The model is not really ONNX-Friendly due the issue mentioned here "https://github.com/qfgaohao/pytorch-ssd/issues/33#issuecomment-467533485"

The Scaled L2 Norm Layer has been replaced with BatchNorm to make the net ONNX compatible.

### Train

The pretrained based is borrowed from https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth .

```bash
python train_ssd.py --datasets ~/data/VOC0712/VOC2007/ ~/data/VOC0712/VOC2012/ --validation_dataset ~/data/VOC0712/test/VOC2007/ --net "vgg16-ssd" --base_net models/vgg16_reducedfc.pth --batch_size 24 --num_epochs 150 --scheduler cosine --lr 0.0012 --t_max 150 --validation_epochs 5
```

### Eval

```bash
python eval_ssd.py --net vgg16-ssd --dataset ~/data/VOC0712/test/VOC2007/ --trained_model models/vgg16-ssd-Epoch-115-Loss-2.819455094383535.pth --label_file models/voc-model-labels.txt
```

## TODO

1. Resnet34 Based Model.
2. BatchNorm Fusion.