SNIPER is an efficient multi-scale training approach for instance-level recognition tasks like object detection and instance-level segmentation. Instead of processing all pixels in an image pyramid, SNIPER selectively processes context regions around the ground-truth objects (a.k.a chips). This significantly speeds up multi-scale training as it operates on low-resolution chips. Due to its memory efficient design, SNIPER can benefit from Batch Normalization during training and it makes larger batch-sizes possible for instance-level recognition tasks on a single GPU. Hence, we do not need to synchronize batch-normalization statistics across GPUs and we can train object detectors similar to the way we do image classification!
SNIPER is initially described in the following paper published at NIPS 2018:
SNIPER: Efficient Multi-Scale Training Bharat Singh*, Mahyar Najibi*, and Larry S. Davis (* denotes equal contribution) NIPS, 2018.
- Train with a batch size of 160 images with a ResNet-101 backbone on 8 V100 GPUs
- NO PYTHON LAYERS (Every layer is optimized for large batch sizes in CUDA/C++)
- HALF PRECISION TRAINING with no loss in accuracy
- 5 Images/second during inference on a single V100 GPU, 47.8/68.2 on COCO using ResNet-101 and without training on segmentation masks
- Use the lightweight MobileNetV2 model trained with SNIPER to get 34.3/54.5 on COCO without training on segmentation masks
- The R-FCN-3K branch is also powered by SNIPER. Now 21% better than YOLO-9000 on ImageNetDet. This branch also supports on-the-fly training (in seconds) with very few samples (no bounding boxes needed!)
- Train on OpenImagesV4 (14x bigger than COCO) with ResNet-101 in 3 days on a p3.x16.large AWS instance!
Here are the COCO results for SNIPER trained using this repository. The models are trained on the trainval set (using only the bounding box annotations) and evaluated on the test-dev set.
| network architecture | pre-trained dataset | mAP | mAP@0.5 | mAP@0.75 | mAP@S | mAP@M | mAP@L | |
|---|---|---|---|---|---|---|---|---|
| SNIPER | ResNet-101 | ImageNet | 46.5 | 67.5 | 52.2 | 30.0 | 49.4 | 58.4 |
| SNIPER | ResNet-101 | OpenImagesV4 | 47.8 | 68.2 | 53.6 | 31.5 | 50.4 | 59.8 |
| SNIPER | MobileNetV2 | ImageNet | 34.3 | 54.4 | 37.9 | 18.5 | 36.9 | 46.4 |
You can download the OpenImages pre-trained model by running bash scripts/download_pretrained_models.sh. The SNIPER detectors based on both ResNet-101 and MobileNetV2 can be downloaded by running bash scripts/download_sniper_detector.sh.
SNIPER is released under Apache license. See LICENSE for details.
@article{sniper2018,
title={{SNIPER}: Efficient Multi-Scale Training},
author={Singh, Bharat and Najibi, Mahyar and Davis, Larry S},
journal={NIPS},
year={2018}
}
@article{analysissnip2017,
title={An analysis of scale invariance in object detection-snip},
author={Singh, Bharat and Davis, Larry S},
journal={CVPR},
year={2018}
}
- Installation
- Running the demo
- Training a model with SNIPER
- Evaluting a trained model
- Other methods and branches in this repo (SSH Face Detector, R-FCN-3K, open-images)
- Clone the repository:
git clone --recursive https://github.com/mahyarnajibi/SNIPER.git
- Compile the provided MXNet fork in the repository.
You need to install CUDA, CuDNN, OpenCV, and OpenBLAS. These libraries are set to be used by default in the provided config.mk file in the SNIPER-mxnet repository. You can use the make command to build the MXNet library:
cd SNIPER-mxnet
make -j [NUM_OF_PROCESS] USE_CUDA_PATH=[PATH_TO_THE_CUDA_FOLDER]
If you plan to train models on multiple GPUs, it is optional but recommended to install NCCL and build MXNet with the NCCL support as instructed below:
make -j [NUM_OF_PROCESS] USE_CUDA_PATH=[PATH_TO_THE_CUDA_FOLDER] USE_NCCL=1
In this case, you may also need to set the USE_NCCL_PATH variable in the above command to point to your NCCL installation path.
If you need more information on how to compile MXNet please see here.
- Compile the C++ files in the lib directory. The following script compiles them all:
bash scripts/compile.sh
- Install the required python packages:
pip install -r requirements.txt
For running the demo, you need to download the provided SNIPER model. The following script downloads the SNIPER model and extracts it into the default location:
bash download_sniper_detector.sh
After downloading the model, the following command would run the SNIPER detector with the default configs on the provided sample image:
python demo.py
If everything goes well, the sample detections would be saved as data/demo/demo_detections.jpg.
You can also run the detector on an arbitrary image by providing its path to the script:
python demo.py --im_path [PATH to the image]
However, if you plan to run the detector on multiple images, please consider using the provided multi-process and multi-batch main_test module.
You can also test the provided SNIPER model based on the MobileNetV2 architecture by passing the provided config file as follows:
python demo.py --cfg configs/faster/sniper_mobilenetv2_e2e.yml
For training SNIPER on COCO, you first need to download the pre-trained models and configure the dataset as described below.
Running the following script downloads and extracts the pre-trained models into the default path (data/pretrained_model):
bash download_pretrained_models.sh
Please follow the official COCO dataset website to download the dataset. After downloading the dataset you should have the following directory structure:
data
|--datasets
|--coco
|--annotations
|--images
You can train the SNIPER detector with or without negative chip mining as described below.
Negative chip mining results in a relative improvement in AP (please refer to the paper for the details). To determine the candidate hard negative regions, SNIPER uses pre-computed proposals. It is possible to use any set of proposals for this purpose. However, for COCO, we also provide the pre-computed proposals extracted from a network trained with SNIPER and a short training schedule (i.e. trained for two epochs as described in the paper). The following script downloads the pre-computed proposals and extracts them into the default path (data/proposals):
bash download_sniper_neg_props.sh
After downloading the proposals, you can train the model with SNIPER and default parameters by calling the following script:
python main_train.py
You can disable the negative chip mining by setting the TRAIN.USE_NEG_CHIPS to False. This is especially useful if you plan to try SNIPER on a new dataset or want to shorten the training cycle. In this case, there is no need for using any pre-computed proposals and the training can be started by calling the following command:
python main_train.py --set TRAIN.USE_NEG_CHIPS False
In any case, the default training settings can be overwritten by passing a configuration file (see the configs folder for example configuration files).
The path to the configuration file can be passed as an argument to the above script using the --cfg flag.
It is also possible to set individual configuration key-values by passing --set as the last argument to the module
followed by the desired key-values (i.e. --set key1 value1 key2 value2 ...).
Please note that the default config file has the same settings used to train the released models.
If you are using a GPU with less amount of memory, please consider reducing the training batch size
(by setting TRAIN.BATCH_IMAGES in the config file or passing --set TRAIN.BATCH_IMAGES [DISIRED_VALUE] as the last argument to the module).
Also, multi-processing is used to process the data. For smaller amounts of memory, you may need to reduce the number of
processes and number of threads according to your system (by setting TRAIN.NUM_PROCESS and TRAIN.NUM_THREAD respectively).
Evaluating the provided SNIPER models
The repository provides a set of pre-trained SNIPER models which can be downloaded by running the following script:
bash download_sniper_detector.sh
This script downloads the model weights and extracts them into the expected directory. To evaluate these models on coco test-dev with the default configuration, you can run the following script:
python main_test.py
The default settings can be overwritten by passing the path to a configuration file with the --cfg flag
(See the configs folder for examples). It is also possible to set individual configuration key-values by passing --set as the last argument to the module
followed by the desired key-values (i.e. --set key1 value1 key2 value2 ...).
Please note that the evaluation is performed in a multi-image per batch and parallel model forward setting. In case of lower GPU memory, please consider reducing the batch size for different scales (by setting TEST.BATCH_IMAGES) or reducing the number of parallel jobs (by setting TEST.CONCURRENT_JOBS in the config file).
Evaluating a model trained with this repository
For evaluating a model trained with this repository, you can run the following script by passing the same configuration file used during the training.
The test settings can be set by updating the TEST section of the configuration file (See the configs folder for examples).
python main_test.py --cfg [PATH TO THE CONFIG FILE USED FOR TRAINING]
By default, this would produce a json file containing the detections on the test-dev which can be zipped and uploaded to the COCO evaluation server.
This repo also contains the R-FCN-3k detector.
Please switch to the R-FCN-3k branch for specific instructions.
This repo also contains modules to train on the open-images dataset. Please switch to the openimages2 branch for specific instructions. The detector on OpenImagesV4 was trained with Soft Sampling.
The SSH face detector would be added to this repository soon. In the meanwhile, you can use the code available at the original SSH repository.
