Have you experiment on COCO with SENet？Please release the results first

[nemonameless]

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[qixuxiang]

It is difficult to train senet-154 backbone on coco for my poor GPU resource (ಥ _ ಥ)
The code is still under test. @nemonameless

[qixuxiang]

I tried senet-154 backbone retinanet on COCO dataset, I only trained 1 epoch , Here is epoch_1.pth results:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.147
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.268
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.145
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.077
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.172
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.190
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.200
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.317
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.330
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.170
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471

I am so sorry I am not able to train more time, anyone who have enough GPUs can try it.

You can change lr with formula lr = base_lr / 8 x num_gpus x img_per_gpu / 2,

base_lr is origin lr in config file, in retinanet base_lr = 0.01.