Imageomics / Imageomics/TreeOfLife-toolbox
Add processing background summary to readme
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Description
@egrace479 I agree with the suggested features. Here are some details on the software/model that's being used on the backend for these pipelines:
Model Inference
- Human-face identification: timesler/facenet-pytorch
- Museum specimen image filtering:
CLIP ViT-L/14@336px - Camera trap image filtering:
Megadetector MDV6-yolov10-e
Apache Spark
- Citizen science image filtering pipeline: rule-based search
- PDQ hash-based image deduplication: faustomorales/pdqhash-python
Originally posted by @NetZissou in https://github.com/Imageomics/bioclip-ecosystem/pull/8#discussion_r2774271282
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Locate the repository README and review the provided processing details, including the model-inference tools, Apache Spark pipelines, and PDQ hash deduplication link. Add a concise background summary using the information in the issue, then verify that the README clearly describes these processing components.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- spark
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100