Open wenjun90 opened 4 years ago
build_fcos_efficientnet_fpn_backbone epoch:6 batchsize:8 gpus:4 coco results: |
AP | AP50 | AP75 | APs | APm | APl |
---|---|---|---|---|---|---|
29.017 | 45.281 | 30.911 | 16.333 | 31.203 | 38.439 |
Could you share me FPS ou prediction time, please? Thank you so much.
sorry, I have no time to do this, it is easy to measure the speed and you can test the model yourself.
def compute_speed(model, input_size, device, iteration):
print('input_size', input_size)
torch.cuda.set_device(device)
torch.backends.cudnn.benchmark = True
model.eval()
model = model.cuda()
input = torch.randn(*input_size).cuda()
for _ in range(10):
model(input)
print('=========Speed Testing=========')
torch.cuda.synchronize()
torch.cuda.synchronize()
t_start = time.time()
for _ in range(iteration):
model(input)
torch.cuda.synchronize()
torch.cuda.synchronize()
elapsed_time = time.time() - t_start
print(
'Elapsed time: [%.2f s / %d iter]' % (elapsed_time, iteration))
print('Speed Time: %.2f ms / iter FPS: %.2f' % (
elapsed_time / iteration * 1000, iteration / elapsed_time))
Dir sxhxliang,
Could I ask you the mAP and FPS you obtain with detectron_efficientNet on COCO dataset?
Thank you