Comments (5)
Dear Authors,
Can you please provide the train script for the model, as it would be really helpful for see the performance of the model on other datasets.
Regards,
from midas.
There are no plans to release the training code.
I'm not sure if I understand your question right. The algorithm that we are using is based on multiple gradient descent. As such it converges to a Pareto-stationary point, which means that there is no update if you are in a pareto-optimal point. However, in practice this is only approximately true, as we are working with stochastic approximations of the objectives. Did you have a look at the corresponding paper that covers the algorithm (https://arxiv.org/abs/1810.04650)?
from midas.
Dear author's,
Thank you so much for such a great work and for sharing the trained model. The proposed MiDaS model works on diverse scenes with significant performance. This is one of the great contributions for the research community. I would like understand two points here:
(1) The depth map inferred from pre-trained MiDaS model is in the form of inverse depth, even after doing inversion I am not able to get absolute depth. What is the method to get absolute depth from the inverse depth?
(2) The quality of depth predicted from pre-trined MiDaS model per frame is really good but it is also inconsistent/jittery over a video sequence. It would be grateful if the training code is also accessible so that MiDaS model can be improved further.
from midas.
@Sankar-CV See for example #42 for answers surrounding relative depth.
There are still no plans to release the training code.
from midas.
Closing due to inactivity.
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Related Issues (20)
- module 'cv2.cv2' has no attribute 'COLORMAP_INFERNO'
- What is the loss function?
- Copy of MiDaS model made with `copy.deepcopy` does not work. HOT 1
- swin2_tiny failed to run forward(): RuntimeError: unflatten: Provided sizes [64, 64] don't multiply up to the size of dim 2 (64) in the input tensor. HOT 1
- [question] Any suggestions on normalizing the outputs better? HOT 9
- Error Loading Pre-trained Weights: Size Mismatch in DPTDepthModel when trying to run for first time. HOT 7
- System crash when loading DPT_Hybrid
- PyTorch Pipeline Broken HOT 2
- gibberish output? HOT 4
- DPT 3.1 models are now available in the Transformers library HOT 1
- Question about COCO dataset HOT 1
- DEPTH VALUE OF THE EACH PIXEL HOT 12
- iOS Demo app is slowing down over time, and the first inference seems much slower HOT 2
- Converted MiDaS 2.1 TFLite model get wrong result on Mobile HOT 5
- MiDaS 2.1 TFLite fp16 with Core ML Delegate gets wrong results
- Cant find pretrained model HOT 1
- Imp. (Improvement) description in the documents
- Exact distance of image HOT 7
- Can't see output...
- New ‘ModuleNotFoundError’ HOT 1
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from midas.