Signs Computer Vision Project

dhruti

Updated 9 months ago

27

views

4

downloads
Classes (26)
* 50% probability of horizontal flip
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Random Gaussian blur of between 0 and 1.25 pixels
* Random brigthness adjustment of between -25 and +25 percent
* Random rotation of between -5 and +5 degrees
* Random shear of between -5° to +5° horizontally and -5° to +5° vertically
* Randomly crop between 0 and 20 percent of the image
* Resize to 416x416 (Stretch)
15
16
17
18
19
20
21
22
23
24
25
==============================
American Sign Language Letters - v1 v1
It includes 1728 images.
Letters are annotated in YOLO v5 PyTorch format.
The following augmentation was applied to create 3 versions of each source image:
The following pre-processing was applied to each image:
This dataset was exported via roboflow.ai on October 20, 2020 at 4:54 PM GMT

Metrics

Try This Model
Drop an image or

A description for this project has not been published yet.

Use This Trained Model

Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.

Supervision

Build Computer Vision Applications Faster with Supervision

Visualize and process your model results with our reusable computer vision tools.

Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            signs-hf0hm_dataset,
                            title = { Signs Dataset },
                            type = { Open Source Dataset },
                            author = { dhruti },
                            howpublished = { \url{ https://universe.roboflow.com/dhruti-apbiv/signs-hf0hm } },
                            url = { https://universe.roboflow.com/dhruti-apbiv/signs-hf0hm },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { feb },
                            note = { visited on 2024-11-14 },
                            }