Comments (3)
Hi, to allow for real-time visualization online without GPU like on the webpage, we saved out the dense correspondences from the trained model as png files and then visualized them in Java. If you don't want to compute the dense correspondences at once in the beginning, and don't care about being real-time too much and can test on GPUs, we also have a jupiter notebook that allows inspecting the prediction at a given clicked location. Let me know which you'd like, and I can release it after I dig it up and clean it a bit.
from omnimotion.
Hi, to allow for real-time visualization online without GPU like on the webpage, we saved out the dense correspondences from the trained model as png files and then visualized them in Java. If you don't want to compute the dense correspondences at once in the beginning, and don't care about being real-time too much and can test on GPUs, we also have a jupiter notebook that allows inspecting the prediction at a given clicked location. Let me know which you'd like, and I can release it after I dig it up and clean it a bit.
I sincerely apologize for the delayed response, and I would like to express my utmost gratitude for your answer. I believe I would prefer the second option, which allows testing on a GPU. However, the first option also seems remarkable. Please forgive my eagerness. Your work has truly fascinated me, and I am eager to learn more about this remarkable achievement. Thank you very much for your assistance.
from omnimotion.
Hi, to allow for real-time visualization online without GPU like on the webpage, we saved out the dense correspondences from the trained model as png files and then visualized them in Java. If you don't want to compute the dense correspondences at once in the beginning, and don't care about being real-time too much and can test on GPUs, we also have a jupiter notebook that allows inspecting the prediction at a given clicked location. Let me know which you'd like, and I can release it after I dig it up and clean it a bit.
Hello, after training with the processed video sequence you provided, a file named "Swing_Data_100000_0_trails.mp4" will be generated. This file displays the trajectories of tracked points. However, when I preprocess the Davis video using the method you provided and train it, the resulting trained model does not have this file. Do I need to perform visualization myself, or is there a problem with my steps?
from omnimotion.
Related Issues (20)
- preparation of custom data set
- Training and evaluating model on TAP-Vid DAVIS produces different results HOT 10
- How to accelerate training speed? HOT 1
- Reporting mistakes during training HOT 2
- A question about blending weight. HOT 9
- The TAPNet loader Module
- The given checkpoint do not match all the model, and it's hard to reproduce the result HOT 6
- Question about the depth consistency loss HOT 2
- Train all frames or sample some? HOT 2
- Particle Tracking Results HOT 3
- Does it have to be trained and optimized for every new video? HOT 1
- the frame resolution when evaluating on TAP-Vid HOT 1
- Will the model weights and testing code be open-sourced HOT 1
- Transformation matrix
- Evaluate the trained checkpoints and the provided checkpoints, and the results of the metrics are inconsistent
- What may be the reason for not generating visual trajectories.
- Can I transform the input of INN into three-dimensional coordinates with an additional fixed fourth dimension?
- track fast moving objects
- Could you share the code for RAFT-C and RAFT-D evaluation in table 1? Thank you! HOT 1
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