We provided pre-trained models for selected FBNet models.
* All the models are trained from scratched with BN using the training schedule specified below.
* Evaluation is performed on a single NVIDIA V100 GPU with `MODEL.RPN.POST_NMS_TOP_N_TEST` set to `200`.
The following inference time is reported:
* inference total batch=8: Total inference time including data loading, model inference and pre/post preprocessing using 8 images per batch.
* inference model batch=8: Model inference time only and using 8 images per batch.
* inference model batch=1: Model inference time only and using 1 image per batch.
* inferenee caffe2 batch=1: Model inference time for the model in Caffe2 format using 1 image per batch. The Caffe2 models fused the BN to Conv and purely run on C++/CUDA by using Caffe2 ops for rpn/detection post processing.
The pre-trained models are available in the link in the model id.
backbone | type | resolution | lr sched | im / gpu | train mem(GB) | train time (s/iter) | total train time (hr) | inference total batch=8 (s/im) | inference model batch=8 (s/im) | inference model batch=1 (s/im) | inference caffe2 batch=1 (s/im) | box AP | mask AP | model id
This follows the [scheduling rules from Detectron.](https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14-L30)
Note that we have multiplied the number of iterations by 8x (as well as the learning rate schedules),
and we have divided the learning rate by 8x.
We also changed the batch size during testing, but that is generally not necessary because testing
requires much less memory than training.
## Multi-GPU training
We use internally `torch.distributed.launch` in order to launch
multi-gpu training. This utility function from PyTorch spawns as many
Python processes as the number of GPUs we want to use, and each Python
Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the `url` LaTeX package.
