CompACT-Deep
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 San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 19 July 2015
- Authors
- Zhaowei Cai, M. Saberian, N. Vasconcelos
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
- AlexNet,VGG16
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
From the paper: For Caltech, we used the training set size of [21] [21] is https://arxiv.org/pdf/1406.1134. From that paper: For the following experiments, we rely solely on the training set of the Caltech Pedestrian Dataset [10]. Of the 71 minute long training videos (∼128k images), we use every fourth video as validation data and the rest for training. 128k * 3/4 = 96k
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 Tesla K40m
- Chips used
- 1
- Power draw
- 286 W
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
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
- SOTA improvement
- Record confidence
- Confident
https://paperswithcode.com/sota/pedestrian-detection-on-caltech "This enables state of the art performance on the Caltech and KITTI datasets, at fairly fast speeds."
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Complexity-Aware Cascades for Deep Pedestrian Detection
- Last updated
- 28 November 2025
What the numbers mean
What this model is
CompACT-Deep was published by University of California San Diego, in United States of America, in July 2015. The organisation is categorised as academia.
It works in Vision, and is recorded as doing object detection.
It is derived from AlexNet,VGG16 rather than trained from scratch, which is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The reason it appears in this catalogue at all is sOTA improvement.
Answers
CompACT-Deep — common questions
Who created CompACT-Deep?
CompACT-Deep was published by University of California San Diego, based in United States of America, categorised as academia.
When was CompACT-Deep released?
CompACT-Deep was published in July 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.
What is CompACT-Deep used for?
CompACT-Deep works in Vision, and is recorded as handling object detection. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run CompACT-Deep?
None. CompACT-Deep 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.
Is CompACT-Deep open source?
No. CompACT-Deep has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CompACT-Deep have?
No parameter count has been published for CompACT-Deep, which is why no memory or speed figure appears on this page.
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.