| Model | Original Paper | ChainerCV | TorchCV* |
|---|---|---|---|
| SSD300@voc07_test | 74.3% | 77.8% | 76.68% |
| SSD512@voc07_test | 76.8% | 79.2% | 78.89% |
| FPNSSD512@voc07_test | - | - | 81.46% |
The accuracy of TorchCV SSD is ~1% lower than ChainerCV. This is because the VGG base model I use performs slightly worse.
I did the experiment by replacing pytorch/vision VGG16 model with the model used in ChainerCV, the SSD512 model got 79.85% accuracy.
FPNSSD512 is created by replacing SSD VGG16 network with FPN50, the rest is the same. It beats all SSD models.
You can download the trained params here.
Our FPNSSD512 model achieved the 1st place on the PASCAL VOC 2012 dataset (by 2018-2-6).
Check the learderboard.
- SSD300
- SSD512
- FPNSSD512
- YOLOV2
- RetinaNet