OwusuBlessing
56 lines
3.0 KiB
Markdown
56 lines
3.0 KiB
Markdown
# Smart Farm Photo Keyword Tagging AI
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## Project Overview
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This project aims to automate the generation of high-quality, agriculture-relevant keyword tags for agricultural stock photos using AI. The system will replace the current manual keyword tagging process, saving significant time and improving consistency.
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## What is Expected
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- **AI Model**: A model trained to generate 5–10 relevant, high-quality keywords per image, with a focus on agricultural context and subtle distinctions (e.g., farmer vs. rancher, male vs. female farmer).
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- **Title Generation**: Optionally generate a descriptive product title for each photo (e.g., "Farmer and son walking in cornfield").
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- **Location Extraction**: If location metadata is present in the image, extract and use it as a keyword (e.g., "Iowa").
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- **CSV Output**: For each photo, output a CSV row with:
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- Photo file name
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- Human-entered keywords (for comparison)
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- AI-generated keywords
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- AI-generated title (if available)
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- Location (if available)
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- **Training**: The system should be trainable on a dataset of ~30,000 currently keyword-tagged photos.
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- **Scalability**: Should handle at least 1,000 photos/month (in batches of 500), with potential to double in 3 years.
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- **Quality**: Keywords and titles must be accurate, relevant, and reflect subtle ag-specific concepts.
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## Folder Structure
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```
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.
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├── data/ # Datasets: training, validation, test images, and CSVs
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│ ├── raw/ # Raw, unprocessed images and metadata
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│ ├── processed/# Preprocessed data ready for modeling
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│ └── ...
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├── notebooks/ # Jupyter notebooks for EDA, prototyping, and experiments
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├── src/ # Source code
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│ ├── data/ # Data loading, preprocessing scripts
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│ ├── model/ # Model architecture, training, inference code
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│ ├── utils/ # Utility functions
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│ └── main.py # Main entry point for training/inference
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├── outputs/ # Generated outputs (CSVs, predictions, logs)
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├── docs.txt # Project requirements and notes
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├── README.md # Project overview and instructions
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└── .gitignore # Files and folders to ignore in git
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```
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### Directory Details
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- **data/**: All datasets. Use `raw/` for original files, `processed/` for cleaned/ready-to-use data.
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- **notebooks/**: Jupyter notebooks for data exploration, prototyping, and model development.
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- **src/**: All source code, organized by function (data, model, utils). `main.py` is the main script.
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- **outputs/**: All generated outputs, including CSVs with AI-generated tags/titles, logs, and model predictions.
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- **docs.txt**: The original requirements and project notes.
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- **README.md**: This file.
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- **.gitignore**: Keeps unnecessary files out of version control.
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## Deliverables
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- Well-documented code in `src/`
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- At least one Jupyter notebook showing EDA and model prototyping
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- Example CSV output as described above
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- Instructions for running the system
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- (Optional) Trained model weights
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## Deadline
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**All deliverables are expected within 3 days of project start.** |