- Download PASCAL VOC dataset (2007 or 2012) and extract at
./data
- Install necessary dependencies:
pip install -r requirements.txt
Arguments for the training script:
>> python train.py --help
usage: train.py [-h] [--data-dir DATA_DIR] [--data-year DATA_YEAR]
[--arch ARCH] [--batch-size BATCH_SIZE]
[--num-batches NUM_BATCHES] [--neg-ratio NEG_RATIO]
[--initial-lr INITIAL_LR] [--momentum MOMENTUM]
[--weight-decay WEIGHT_DECAY] [--num-epochs NUM_EPOCHS]
[--checkpoint-dir CHECKPOINT_DIR]
[--pretrained-type PRETRAINED_TYPE] [--gpu-id GPU_ID]
Arguments explanation:
-
--data-dir
dataset directory (must specify to VOCdevkit folder) -
--data-year
the year of the dataset (2007 or 2012) -
--arch
SSD network architecture (ssd300 or ssd512) -
--batch-size
training batch size -
--num-batches
number of batches to train (-1
: train all) -
--neg-ratio
ratio used in hard negative mining when computing loss -
--initial-lr
initial learning rate -
--momentum
momentum value for SGD -
--weight-decay
weight decay value for SGD -
--num-epochs
number of epochs to train -
--checkpoint-dir
checkpoint directory -
--pretrained-type
pretrained weight type (base
: using pretrained VGG backbone, other options: see testing section) -
--gpu-id
GPU ID -
how to train SSD300 using PASCAL VOC2007 for 100 epochs:
python train.py --data-dir ./data/VOCdevkit --data-year 2007 --num-epochs 100
- how to train SSD512 using PASCAL VOC2012 for 120 epochs on GPU 1 with batch size 8 and save weights to
./checkpoints_512
:
python train.py --data-dir ./data/VOCdevkit --data-year 2012 --arch ssd512 --num-epochs 120 --batch-size 8 --checkpoint_dir ./checkpoints_512 --gpu-id 1
Arguments for the testing script:
>> python test.py --help
usage: test.py [-h] [--data-dir DATA_DIR] [--data-year DATA_YEAR]
[--arch ARCH] [--num-examples NUM_EXAMPLES]
[--pretrained-type PRETRAINED_TYPE]
[--checkpoint-dir CHECKPOINT_DIR]
[--checkpoint-path CHECKPOINT_PATH] [--gpu-id GPU_ID]
Arguments explanation:
-
--data-dir
dataset directory (must specify to VOCdevkit folder) -
--data-year
the year of the dataset (2007 or 2012) -
--arch
SSD network architecture (ssd300 or ssd512) -
--num-examples
number of examples to test (-1
: test all) -
--checkpoint-dir
checkpoint directory -
--checkpoint-path
path to a specific checkpoint -
--pretrained-type
pretrained weight type (latest
: automatically look for newest checkpoint incheckpoint_dir
,specified
: use the checkpoint specified incheckpoint_path
) -
--gpu-id
GPU ID -
how to test the first training pattern above using the latest checkpoint:
python test.py --data-dir ./data/VOCdevkit --data-year 2007 --checkpoint_dir ./checkpoints
- how to test the second training pattern above using the 100th epoch's checkpoint, using only 40 examples:
python test.py --data-dir ./data/VOCdevkit --data-year 2012 --arch ssd512 --checkpoint_path ./checkpoints_512/ssd_epoch_100.h5 --num-examples 40
- Single Shot Multibox Detector paper: paper
- Caffe original implementation: code
- Pytorch implementation: [code] (https://github.com/ChunML/ssd-pytorch)