SSD
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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.
- Published
- 8 December 2015
- Authors
- Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
- Approach
- Supervised
- Base model
- VGG16
- Numerical format
- FP32
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
- 2,300,000 tokens
Multiple datasets were used (PASCAL VOC, ILSVRC, COCO) but afaict COCO is the largest. Per this paper https://arxiv.org/abs/1703.06870v1, final paragraph before section 4.1, "As in previous work [3, 21], we train using the union of 80k train images and a 35k subset of val images (trainval35k), and report ablations on the remaining 5k subset of val images (minival)". So trainval35k is 80k + 35k = 115k
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
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Confident
- Citations
- 39,468
Also listed in Denis Panjuta's List of 100+ AI Algorithms
Sources
Where this record came from and when it was last checked.
- Reference
- SSD: Single Shot MultiBox Detector
- Last updated
- 28 November 2025
What the numbers mean
What this model is
SSD was published by its authors, during December 2015.
It works in the domain of Vision, and is recorded as performing the task of object detection.
Rather than being trained from scratch, it is derived from VGG16. That is why it shares the base model's general shape and size.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
How it was trained
The training set ran to roughly 2,300,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
SSD — common questions
SSD— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
SSD— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
SSD— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
SSD— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
SSD— 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.
SSD— when was it released?
It was published in December 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.