Closed DannyGJdeJong closed 3 years ago
remove the code above. I plan to modify the head part. (#47 ) But, I'm busy these days... So, if it is difficult to wait for the update, you will have to write your own head.
On yolov4 v3.0.0, you can enable mixed precision.
yolo.predict
or yolo.inference
use yolo_diou_nms
.
But, this allows only np.float32
type array. So if you want to enable mixed precision, modify the code shown below.
if candidates is not np.float32:
candidates = candidates.astype(np.float32)
pred_bboxes = self.yolo_diou_nms(
candidates=candidates, beta_nms=self.config.yolo_0.beta_nms
)
Is it possible to use TensorFlow's mixed precision for a performance boost on newer nvidia graphics cards?
Right now it throws a TypeError when trying to do this because the outputs and inputs will not match in the existing model.
Am I doing something wrong in enabling it this way or is it just not possible with the current implementation?