snakes Instance Segmentation Dataset and Pre-Trained Model by snake

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1028 images

Explore Dataset

In the current day and age where we are bombarded with thousands of digital text, images and videos daily, we are bound to come across text, images or videos that we do not want or should not see (including snakes!).

Under constant influence of such texts, images and/or videos, a person may experience the following short term effect

Sweating / Chills
Shortness of breath
Nausea / Headaches / Numbness
Confusion or disorientation
Loss of appetite

Therefore, how can we utilise machine learning to block off and filter out snakes (pixel by pixel) from images and videos?

In this project, we utilise Roboflow to assist with annotating of our project using the smart polygon features. After annotation, we utilise the dataset to train our customised YOLOv7 model. Our deployed model can be found streamlit at: https://erjieyong-v-instance-segmentation-streamlitstreamlit-app-2r2fpv.streamlit.app/

Active learning is engaged to continuously push low confidence image output back to roboflow for further training!

Learn more about this project at my github: https://github.com/erjieyong/ViShield_Computer_Vision

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            snakes-oozrc_dataset,
                            title = { snakes Dataset },
                            type = { Open Source Dataset },
                            author = { snake },
                            howpublished = { \url{ https://universe.roboflow.com/snake-kumjy/snakes-oozrc } },
                            url = { https://universe.roboflow.com/snake-kumjy/snakes-oozrc },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-04-26 },
                            }