YOLOv8 (reCAPTCHA fine-tuned)
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
- ETH Zurich
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 13 September 2024
- Authors
- Andreas Plesner, Tobias Vontobel, Roger Wattenhofer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Image segmentation
- Base model
- YOLOv8x
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
Combined with the public data, this resulted in around 14k image/label pairs for fine-tuning the classification model.
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.
- Training code
- Open (non-commercial)
https://github.com/aplesner/Breaking-reCAPTCHAv2 no clear license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Breaking reCAPTCHAv2
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
YOLOv8 (reCAPTCHA fine-tuned) was published by ETH Zurich, in Switzerland, in September 2024. academia is the category the publisher falls under.
It works in Vision, and is recorded as doing image classification, Image segmentation.
Its starting point was YOLOv8x — most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
YOLOv8 (reCAPTCHA fine-tuned) — common questions
Is YOLOv8 (reCAPTCHA fine-tuned) open source?
The licensing for YOLOv8 (reCAPTCHA fine-tuned) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does YOLOv8 (reCAPTCHA fine-tuned) have?
No parameter count has been published for YOLOv8 (reCAPTCHA fine-tuned), which is why no memory or speed figure appears on this page.
Who created YOLOv8 (reCAPTCHA fine-tuned)?
YOLOv8 (reCAPTCHA fine-tuned) was published by ETH Zurich, based in Switzerland, categorised as academia.
When was YOLOv8 (reCAPTCHA fine-tuned) released?
YOLOv8 (reCAPTCHA fine-tuned) was published in September 2024. 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 YOLOv8 (reCAPTCHA fine-tuned) used for?
YOLOv8 (reCAPTCHA fine-tuned) works in Vision, and is recorded as handling image classification, Image segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run YOLOv8 (reCAPTCHA fine-tuned)?
None. YOLOv8 (reCAPTCHA fine-tuned) 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.
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.