Project Info
This project did not submit a demo video on Devpost.
Our project was inspired by the innovative work showcased at an Intel workshop, which harnessed the power of image recognition for wildfire prediction and even the identification of ancient fossils. This ingenuity sparked our desire to create a groundbreaking skin model. Our goal was to develop a AI solution that could analyze user-submitted skin photos, providing not just a diagnosis but also essential information on disease risks and potential treatments. Our journey was marked by challenges, with the primary hurdle being the fine-tuning of the AI model. We encountered difficulties stemming from dependencies, requiring relentless problem-solving. Additionally, we faced intermittent connectivity issues with Intel's cloud service and Jupyter Notebook, which occasionally disrupted our training process. Despite these obstacles, we remained resolute in our mission to deliver a valuable tool for the early detection of skin diseases.
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Analysis
View
Metric
- 13
- 4
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- HTMLIn code
- PythonIn code
- FlaskClaimed
- PyTorchClaimed
3 of 5 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
17 KB
Source files
15
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
isa-hello/Skintelligence
16 files · 884 KB · @ 7b1ba13
Structure
Interface
3 files · 19%Screens, components and styles rendered to the user.
Application logic
1 file · 6%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- HTML55%
- CSS39%
- Python6%
Share of indexed source by file size. Binary and vendored files are excluded.
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