PASCAL VOC2007 Test
| Model | Original | Ours (weight conversion) | Ours (train) |
|---|---|---|---|
| SSD300 | 77.5 % [2] | 77.8 % | 77.5 % / 77.6 % (4 GPUs) |
| SSD512 | 79.5 % [2] | 79.7 % | 80.1 % * / 80.5 % (4 GPUs) |
Scores are mean Average Precision (mAP) with PASCAL VOC2007 metric.
*: We set batchsize to 24 because of memory limitation. The original paper used 32.
Detect objects in an given image. This demo downloads Pascal VOC pretrained model automatically if a pretrained model path is not given.
$ python demo.py [--model ssd300|ssd512] [--gpu <gpu>] [--pretrained-model <model_path>] <image>.jpg
Convert *.caffemodel to *.npz. Some layers are renamed to fit ChainerCV. SSD300 and SSD512 are supported.
$ python caffe2npz.py <source>.caffemodel <target>.npz
The evaluation can be conducted using chainercv/examples/detection/eval_detection.py.
You can train the model with the following code.
Note that this code requires cv2 module.
$ python train.py [--model ssd300|ssd512] [--batchsize <batchsize>] [--gpu <gpu>]
If you want to use multiple GPUs, use train_multi.py.
Note that this code requires chainermn module.
$ mpiexec -n <#gpu> python train_multi.py [--model ssd300|ssd512] [--batchsize <batchsize>] [--test-batchsize <batchsize>]
You can download weights that were trained by ChainerCV.
- Wei Liu et al. "SSD: Single shot multibox detector" ECCV 2016.
- Cheng-Yang Fu et al. "DSSD : Deconvolutional Single Shot Detector" arXiv 2017.