Author | open – mmlab compile | Flin source | dead simple
Benchmarks and Model Zoo
The environment
hardware
- 8 NVIDIA Tesla V100 GPUs
- Intel Xeon 4114 CPU @ 2.20GHz
Software environment
- Python 3.6/3.7
- PyTorch 1.1
- CUDA 9.0.176
- CUDNN 7.0.4
- NCCL 2.1.15
Mirror sites
We use AWS as the primary site for hosting Model Zoo and maintain the image on Ali Cloud. You can replace s3.ap-northeast-2.amazonaws.com/open-mmlab with…
Commonly used Settings
- All FPN and RPN-C4 benchmarks were trained using 8 Gpus with a batch size of 16(2 images per GPU). The other C4 baselines were trained with 8 Gpus of batch size 8 (1 image per GPU).
- All the models are here
coco_2017_trainOn training as well as incoco_2017_valThe test. - We use distributed training, and BN level statistics are fixed.
- We use the same training schedule as Detectron. 1X represents 12 epochs, while 2X represents 24 Epochs, which is slightly less than the number of iterations of Detectron and negligible.
- All of the PyTorch style pretraining trunks on ImageNet come from PyTorchmodel Zoo.
- For a fair comparison with other code bases, we report GPU memory
torch.cuda.max_memory_allocated()Is the maximum value of all eight Gpus. Note that this value is usually less thannvidia-smiThe value displayed. - We report reasoning time as total time, including data loading, network forwarding, and post-processing.
The baseline
More models with different trunks will be added to the Model Zoo.
RPN
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | AR1000 | Download |
|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | 1x | – | – | 20.5 | 51.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | 2x | 2.2 | 0.17 | 20.3 | 52.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 1x | – | – | 20.1 | 50.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 2x | – | – | 20.0 | 51.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | 1x | 3.3 | 0.253 | 16.9 | 58.2 | – |
| R-50-FPN | pytorch | 1x | 3.5 | 0.276 | 17.7 | 57.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 2x | – | – | – | 57.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | 1x | 5.2 | 0.379 | 13.9 | 59.4 | – |
| R-101-FPN | pytorch | 1x | 5.4 | 0.396 | 14.4 | 58.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 2x | – | – | – | 59.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 6.6 | 0.589 | 11.8 | 59.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 2x | – | – | – | 59.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 9.5 | 0.955 | 8.3 | 59.8 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 2x | – | – | – | 60.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
Faster R-CNN
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | Download |
|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | 1x | – | – | 9.5 | 34.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | 2x | 4.0 | 0.39 | 9.3 | 36.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 1x | – | – | 9.3 | 33.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 2x | – | – | 9.4 | 35.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | 1x | 3.6 | 0.333 | 13.5 | 36.6 | – |
| R-50-FPN | pytorch | 1x | 3.8 | 0.353 | 13.6 | 36.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 2x | – | – | – | 37.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | 1x | 5.5 | 0.465 | 11.5 | 38.8 | – |
| R-101-FPN | pytorch | 1x | 5.7 | 0.474 | 11.9 | 38.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 2x | – | – | – | 39.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 6.9 | 0.672 | 10.3 | 40.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 2x | – | – | – | 40.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 9.8 | 1.040 | 7.3 | 41.3 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 2x | – | – | – | 40.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| HRNetV2p-W18 | pytorch | 1x | – | – | – | 36.1 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W18 | pytorch | 2x | – | – | – | 38.3 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 1x | – | – | – | 39.5 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 2x | – | – | – | 40.6 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 1x | – | – | – | 40.9 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 2x | – | – | – | 41.5 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
Mask R-CNN
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | mask AP | Download |
|---|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | 1x | – | – | 8.1 | 35.9 | 31.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | 2x | 4.2 | 0.43 | 8.1 | 37.9 | 32.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 1x | – | – | 7.9 | 35.1 | 31.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | 2x | – | – | 8.0 | 37.2 | 32.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | 1x | 3.8 | 0.430 | 10.2 | 37.4 | 34.3 | – |
| R-50-FPN | pytorch | 1x | 3.9 | 0.453 | 10.6 | 37.3 | 34.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 2x | – | – | – | 38.5 | 35.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | 1x | 5.7 | 0.534 | 9.4 | 39.9 | 36.1 | – |
| R-101-FPN | pytorch | 1x | 5.8 | 0.571 | 9.5 | 39.4 | 35.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 2x | – | – | – | 40.3 | 36.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 7.1 | 0.759 | 8.3 | 41.1 | 37.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 2x | – | – | – | 41.4 | 37.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 1.102 | 6.5 | 42.1 | 38.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 2x | – | – | – | 42.0 | 37.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| HRNetV2p-W18 | pytorch | 1x | – | – | – | 37.3 | 34.2 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W18 | pytorch | 2x | – | – | – | 39.2 | 35.7 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 1x | – | – | – | 40.7 | 36.8 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 2x | – | – | – | 41.7 | 37.5 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 1x | – | – | – | 42.4 | 38.1 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 2x | – | – | – | 42.9 | 38.3 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
Fast R-CNN (with pre-calculated proposals)
| Backbone | Style | type | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | mask AP | Download |
|---|---|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | Faster | 1x | – | – | 6.7 | 35.0 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | Faster | 2x | 3.8 | 0.34 | 6.6 | 36.4 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | Faster | 1x | – | – | 6.3 | 34.2 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | Faster | 2x | – | – | 6.1 | 35.8 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | Faster | 1x | 3.3 | 0.242 | 18.4 | 36.6 | – | – |
| R-50-FPN | pytorch | Faster | 1x | 3.5 | 0.250 | 16.5 | 35.8 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | Mask | 1x | – | – | 8.1 | 35.9 | 31.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | caffe | Mask | 2x | 4.2 | 0.43 | 8.1 | 37.9 | 32.9 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | Mask | 1x | – | – | 7.9 | 35.1 | 31.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-C4 | pytorch | Mask | 2x | – | – | 8.0 | 37.2 | 32.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | Faster | 2x | – | – | – | 37.1 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | Faster | 1x | 5.2 | 0.355 | 14.4 | 38.6 | – | – |
| R-101-FPN | pytorch | Faster | 1x | 5.4 | 0.388 | 13.2 | 38.1 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | Faster | 2x | – | – | – | 38.8 | – | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | Mask | 1x | 3.4 | 0.328 | 12.8 | 37.3 | 34.5 | – |
| R-50-FPN | pytorch | Mask | 1x | 3.5 | 0.346 | 12.7 | 36.8 | 34.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | Mask | 2x | – | – | – | 37.9 | 34.8 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | Mask | 1x | 5.2 | 0.429 | 11.2 | 39.4 | 36.1 | – |
| R-101-FPN | pytorch | Mask | 1x | 5.4 | 0.462 | 10.9 | 38.9 | 35.8 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | Mask | 2x | – | – | – | 39.9 | 36.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
RetinaNet
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | Download |
|---|---|---|---|---|---|---|---|
| R-50-FPN | caffe | 1x | 3.4 | 0.285 | 12.5 | 35.8 | – |
| R-50-FPN | pytorch | 1x | 3.6 | 0.308 | 12.1 | 35.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 2x | – | – | – | 36.4 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| R-101-FPN | caffe | 1x | 5.3 | 0.410 | 10.4 | 37.8 | – |
| R-101-FPN | pytorch | 1x | 5.5 | 0.429 | 10.9 | 37.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 2x | – | – | – | 38.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 6.7 | 0.632 | 9.3 | 39.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 2x | – | – | – | 39.3 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 9.6 | 0.993 | 7.0 | 40.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 2x | – | – | – | 39.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
Cascade R-CNN
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | Download |
|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | 1x | 8.7 | 0.92 | 5.0 | 38.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | 1x | 3.9 | 0.464 | 10.9 | 40.5 | – |
| R-50-FPN | pytorch | 1x | 4.1 | 0.455 | 11.9 | 40.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 20e | – | – | – | 41.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | 1x | 5.8 | 0.569 | 9.6 | 42.4 | – |
| R-101-FPN | pytorch | 1x | 6.0 | 0.584 | 10.3 | 42.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 20e | – | – | – | 42.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 0.770 | 8.9 | 43.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 20e | – | – | – | 44.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 1.133 | 6.7 | 44.5 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 20e | – | – | – | 44.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| HRNetV2p-W18 | pytorch | 20e | – | – | – | 41.2 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 20e | – | – | – | 43.7 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 20e | – | – | – | 44.6 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
Cascade Mask R-CNN
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | mask AP | Download |
|---|---|---|---|---|---|---|---|---|
| R-50-C4 | caffe | 1x | 9.1 | 0.99 | 4.5 | 39.3 | 32.8 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | caffe | 1x | 5.1 | 0.692 | 7.6 | 40.9 | 35.5 | – |
| R-50-FPN | pytorch | 1x | 5.3 | 0.683 | 7.4 | 41.2 | 35.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 20e | – | – | – | 42.3 | 36.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | caffe | 1x | 7.0 | 0.803 | 7.2 | 43.1 | 37.2 | – |
| R-101-FPN | pytorch | 1x | 7.2 | 0.807 | 6.8 | 42.6 | 37.0 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 20e | – | – | – | 43.3 | 37.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 1x | 8.4 | 0.976 | 6.6 | 44.4 | 38.2 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 20e | – | – | – | 44.7 | 38.6 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 1x | 11.4 | 1.33 | 5.3 | 45.4 | 39.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 20e | – | – | – | 45.7 | 39.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| HRNetV2p-W18 | pytorch | 20e | – | – | – | 41.9 | 36.4 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 20e | – | – | – | 44.5 | 38.5 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 20e | – | – | – | 46.0 | 39.5 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
S:
- The schedule in 20E cascading (mask)R-CNN indicates lr reduction at 16th and 19th epochs, for a total reduction of 20 epochs.
Hybrid Mission Cascade (HTC)
| Backbone | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | mask AP | Download |
|---|---|---|---|---|---|---|---|---|
| R-50-FPN | pytorch | 1x | 7.4 | 0.936 | 4.1 | 42.1 | 37.3 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-50-FPN | pytorch | 20e | – | – | – | 43.2 | 38.1 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| R-101-FPN | pytorch | 20e | 9.3 | 1.051 | 4.0 | 44.9 | 39.4 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-32x4d-FPN | pytorch | 20e | 5.8 | 0.769 | 3.8 | 46.1 | 40.3 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| X-101-64x4d-FPN | pytorch | 20e | 7.5 | 1.120 | 3.5 | 46.9 | 40.8 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| HRNetV2p-W18 | pytorch | 20e | – | – | – | 43.1 | 37.9 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W32 | pytorch | 20e | – | – | – | 45.3 | 39.6 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 20e | – | – | – | 46.8 | 40.7 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
| HRNetV2p-W48 | pytorch | 28e | – | – | – | 47.0 | 41.0 | model(Open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection…) |
Note:
- For more information and a more powerful model (50.7/43.9), see Mixed task cascading (github.com/open-mmlab/…) .
SSD
| Backbone | Size | Style | Lr schd | Memory (GB) | Training Time (S /iter) | Minimum time (FPS) | box AP | Download |
|---|---|---|---|---|---|---|---|---|
| VGG16 | 300 | caffe | 120e | 3.5 | 0.256 | 25.9/34.6 | 25.7 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
| VGG16 | 512 | caffe | 120e | 7.6 | 0.412 | 20.7/25.4 | 29.3 | model(S3.ap-northeast-2.amazonaws.com/open-mmlab/…) |
Note:
cudnn.benchmarkSet toTrueUsed for SSD training and testing.- For batch size= 1 and Batch size= 8, the reasoning time is reported.
- Because the model parameters and NMS,COCO and VOC speed are different.
Group normalization (GN)
For more information, see group normalization (github.com/open-mmlab/…
Weight standardization
For more information, see weight standardization (github.com/open-mmlab/…
Deformable convolution v2
For more information, see Deformable convolutional Networks (github.com/open-mmlab/…
CARAFE: Content-aware reorganization of functionality
For more information, please refer to CARAFE(github.com/open-mmlab/…
Instaboost
For more information, see Instaboost(github.com/open-mmlab/…
Libra R-CNN
For more information, see Libra R-CNN(github.com/open-mmlab/…
Guided Anchoring
For more information, see Guided Anchoring(github.com/open-mmlab/…
FCOS
For more information, see FCOS(github.com/open-mmlab/…
FoveaBox
For more information, see FoveaBox(github.com/open-mmlab/…
RepPoints
For more information, refer to RepPoints(github.com/open-mmlab/…
FreeAnchor
For more information, see FreeAnchor(github.com/open-mmlab/…
Grid R-CNN (plus)
For more information, see Grid R-CNN(github.com/open-mmlab/…
GHM
For more information, see GHM(github.com/open-mmlab/…
GCNet
For more information, please refer to GCNet(github.com/open-mmlab/…
HRNet
For more information, please refer to HRNet(github.com/open-mmlab/…
Mask Scoring R-CNN
For more information, please refer to Mask Scoring R-CNN(github.com/open-mmlab/…
Train from Scratch
For more information, see Rethinking ImageNet pre-training (github.com/open-mmlab/…
NAS-FPN
For more information, see NAS-FPN(github.com/open-mmlab/…
ATSS
For more information, please refer to ATSS(github.com/open-mmlab/…
Other data sets
We are also interested in PASCAL VOC(github.com/open-mmlab/… (github.com/open-mmlab/ MMDetection/blob/Master/Configs/Cityscapes) and WIDER FACE(github.com/open-mmlab/…
Comparison with Detectron and MasKRCNN-Benchmark
We combine MMDetection with Detectron(github.com/facebookres…) And maskrcnn – benchmark (github.com/facebookres…
In general, MMDetection has three advantages over Detectron.
- Higher performance (especially for Mask AP)
- Faster training speed
- Efficient memory
performance
Detectron and MaskrCNN-Benchmark use Caffe-style ResNet as their backbone. We use caffe style (weights from (github.com/facebookres…) And PyTorch style (weights from official Model Zoo)ResNet trunk reports results, represented as PyTorch style results/Caffe style results.
We found that PyTorch style ResNet generally converges more slowly than Caffe style ResNet, resulting in slightly lower results at 1x progress, but higher final results at 2x progress.
| type | Lr schd | Detectron | maskrcnn-benchmark | mmdetection |
|---|---|---|---|---|
| RPN | 1x | 57.2 | – | 57.1/58.2 |
| 2x | – | – | 57.6 / – | |
| Faster R-CNN | 1x | 36.7 | 36.8 | 36.4/36.6 |
| 2x | 37.9 | – | 37.7 / – | |
| Mask R-CNN | 1x | 37.7 & 33.9 | 37.8 & 34.2 | 37.3 & 34.2/37.4 & 34.3 |
| 2x | 38.6 & 34.5 | – | 38.5&35.1 / – | |
| Fast R-CNN | 1x | 36.4 | – | 35.8/36.6 |
| 2x | 36.8 | – | 37.1 / – | |
| Fast R-CNN (w/mask) | 1x | 37.3 & 33.7 | – | 36.8 & 34.1/37.3 & 34.5 |
| 2x | 37.7 & 34.0 | – | 37.9&34.8 / – |
Training speed
The training speed is measured in s/ ITER. The lower the better.
| type | Detectron (P1001) | maskrcnn-benchmark (V100) | mmdetection (V1002) |
|---|---|---|---|
| RPN | 0.416 | – | 0.253 |
| Faster R-CNN | 0.544 | 0.353 | 0.333 |
| Mask R-CNN | 0.889 | 0.454 | 0.430 |
| Fast R-CNN | 0.285 | – | 0.242 |
| Fast R-CNN (w/mask) | 0.377 | – | 0.328 |
-
1. Facebook’s Big Basin server (P100 / V100) is slightly faster than the server we use. Mmdetection also runs slightly faster on FB servers.
-
2. For a fair comparison, we’ve listed Caffe’s results here.
Reasoning speed
Inference speed is measured in FPS (img/s) on a single GPU. The higher the better.
| type | Detectron (P100) | maskrcnn-benchmark (V100) | mmdetection (V100) |
|---|---|---|---|
| RPN | 12.5 | – | 16.9 |
| Faster R-CNN | 10.3 | 7.9 | 13.5 |
| Mask R-CNN | 8.5 | 7.7 | 10.2 |
| Fast R-CNN | 12.5 | – | 18.4 |
| Fast R-CNN (w/mask) | 9.9 | – | 12.8 |
The training of memory
| type | Detectron | maskrcnn-benchmark | mmdetection |
|---|---|---|---|
| RPN | 6.4 | – | 3.3 |
| Faster R-CNN | 7.2 | 4.4 | 3.6 |
| Mask R-CNN | 8.6 | 5.2 | 3.8 |
| Fast R-CNN | 6.0 | – | 3.3 |
| Fast R-CNN (w/mask) | 7.9 | – | 3.4 |
There is no doubt that maskrCNN benchmark and MMDetection are more efficient than Detectron for storage, but the main advantage is PyTorch itself. We also performed some memory optimizations to push it forward.
Note that Caffe2 and PyTorch have different apis to get memory usage through different implementations. For all code bases, the memory usage shown by NVIDIa-SMI is greater than the figure reported in the table above.
The original link: mmdetection. Readthedocs. IO/en/latest/M…
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