Mono3D++
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of California Los Angeles (UCLA),Megvii Inc
- Organisation type
- Academia,Industry
- Country
- United States of America, China
- Published
- 11 January 2019
- Authors
- Tong He, Stefano Soatto
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- 3D modeling, Vision
- Task
- 3D segmentation, Object detection
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"We evaluate our method on the KITTI object detection benchmark. This dataset contains 7, 481 training images "
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 4.9 × 10¹⁸ FLOP
- How it was established
- Hardware
(4) * (6.691 * 10**12) * (168* 3600) * (0.3) = (num gpu) * (peak flops) * (time in seconds) * (assumed utilization rate) = training time - about one week = 168 hours from "It takes about one week to train the 2D bounding box net-work, and two hours for the orientation/3D scale network on KITTI with 4 TITAN-X GPUs. The landmark detector is trained on Pascal3D. The training process for the monocu- lar depth estimation network is unsupervised using KITTI stereo-pairs, which takes around 5 to 12 …
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 4
- Chip-hours
- 672
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 2.1 kW
"about one week" from section 3.4
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 135
Sources
Where this record came from and when it was last checked.
- Reference
- Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Mono3D++ was published by University of California Los Angeles (UCLA),Megvii Inc, in the country recorded as United States of America, during January 2019. The category the publisher falls under is academia,Industry.
It works in the domain of 3D modeling, Vision, and is recorded as performing the task of 3D segmentation, Object detection.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took a computation budget of roughly 4.9 × 10¹⁸ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Mono3D++ — common questions
Mono3D++— how much compute was used to train it?
Training consumed around 4.9 × 10¹⁸ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Mono3D++— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Mono3D++— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Mono3D++— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Mono3D++— who created it?
It was published by University of California Los Angeles (UCLA),Megvii Inc, based in United States of America, an organisation categorised as academia,Industry.
Mono3D++— when was it released?
It was published in January 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Mono3D++— what is it used for?
It works in the domain of 3D modeling, Vision, and is recorded as handling the task of 3D segmentation, Object detection. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.