boladeE
a6e52f8ad0
- Added Fast api web application for recycling object detection - Included YOLOv8 model training notebook - Set up project structure with datasets and training directories - Added requirements.txt for dependencies - Configured .gitignore for Python virtual environment and cache files
55 lines
2.0 KiB
Markdown
55 lines
2.0 KiB
Markdown
# DS Task Recycling Project
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This project is a toy project for training and quality assurance purposes. It involves developing a simple Flask API that processes an image (or a hardcoded image) of a motherboard and detects memory modules present on it. The API will return the image with bounding boxes drawn around each detected memory module.
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## Project Overview
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- **Input Types:**
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- Image upload via the Flask API.
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- A hardcoded image for testing purposes.
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- **Dataset:**
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- 20 pictures of motherboards with memory.
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- 20 pictures of motherboards without memory.
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- **Output:**
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- An annotated image with bounding boxes around each detected memory module.
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For example, if there are two memory modules, two boxes are drawn; if only one is detected, then one box is drawn.
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- **Annotation Tool Suggestion:**
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- We suggest using [makesense.ai](https://www.makesense.ai/) for manual annotation if needed.
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## Task Details
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The developer is required to research and answer the following questions as part of the task:
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1. **Algorithm Choice:**
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- Which algorithm will you use for detecting the memory modules?
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- Why do you choose this particular algorithm?
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2. **Hardware Considerations:**
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- Does CPU or GPU have an impact on your decision? Please explain.
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3. **Video Input:**
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- What if a video is provided instead of single images?
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- Does your approach change when processing videos? Please describe your approach.
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## Proposed Flask API Implementation
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1. **API Endpoints:**
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- An endpoint for uploading images which processes and returns the annotated image.
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- An endpoint parameter for using a hardcoded image for testing purposes.
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2. **Processing Workflow:**
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- Receive an image (either via file upload or from a hardcoded source).
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- Apply the chosen object detection algorithm to detect memory modules.
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- Draw bounding boxes around each detected memory module.
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- Return the annotated image to the user.
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## Data Set:
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Dataset in on the `training` folder. And there is `memory` and `no_memory` subfolder in it.
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