Comments (3)
I did not put FPS performance results on README because the frame rate performance largely depends on :
- Hardware (GPU, CPU)
- Cuda and CuDNN versions
- PyTorch and other package versions
- preprocess and postprocess
The papers usually don't disclose all the conditions on which their benchmark was done.
But as long as I see your code, preprocessing to re-size your large image and imwrite (if you do that every frame) should be slow. I recommend you to perform only the forward process on GPU and measure the inference speed firstly.
Thank you!
from pytorch_yolov3.
Thanks a lot!
If I feed 1600x1200 imgs to pytorch_GaussianYOLOv3 , I get 10 fps (the time includes preprossing)
Then I re-size 1600x1200 imgs to 416x416 and imwrite. The original code after imwrite is commented:
img = np.transpose(img / 255., (2, 0, 1))
img = torch.from_numpy(img).float().unsqueeze(0)
if gpu >= 0:
# Send model to GPU
img = Variable(img.type(torch.cuda.FloatTensor))
else:
img = Variable(img.type(torch.FloatTensor))
If I feed 416x416 imgs to pytorch_GaussianYOLOv3, I get 21 fps.
Which of these two types of FPS is usually mentioned on the papers?
from pytorch_yolov3.
I'm not sure but in the following code they measure fps including pre-process.
https://github.com/pjreddie/darknet/blob/master/src/demo.c
from pytorch_yolov3.
Related Issues (20)
- Hi, I simply copy your code and run,but i will also meet this problem.i changed the bctchsize to 8 when training in the visdrone dataset,
- problem about NMS in the evaluation.
- Error when training yolo on my dataset:IndexError: index 69 is out of bounds for dimension 3 with size 68 HOT 4
- how much time do you cost for training with pretrained model? and what the GPU you used? HOT 2
- Issues about the bboxes_iou
- class YOLOV3 forward function return error when using costumed data HOT 1
- Learning rate is low. HOT 1
- zero AP HOT 1
- The evaluation process is too slow!!! HOT 11
- Difference between .ckpt and .pt files HOT 1
- confused about the train loss、size_average and the performance. HOT 6
- Scale gain for loss HOT 2
- IMPORTANT: PyTorch_YOLOv3 will no longer be maintained from April 2020
- multy GPU support? HOT 1
- Low confidence score with high average precision
- Is my code for [sorting the clusters according to areas] and [matching clusters with ANCH_MASK] right?
- Why BCELoss for xy? HOT 2
- How to train custom dataset?
- On License
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from pytorch_yolov3.