Datasets:
Tasks:
Image Classification
Formats:
parquet
Size:
1K - 10K
Tags:
fish-recognition
fine-grained-recognition
biodiversity-informatics
benchmark
temporal-evaluation
License:
| # QT26-QC canonical evaluation protocol | |
| ## Submission | |
| Submit one TSV row per `public_id` with columns `public_id`, `top1`, ..., `top20`. | |
| Prediction values are FishBase 25.04 `canonical_taxon_key` strings. All 6,719 | |
| canonical IDs must occur exactly once. | |
| If several submitted labels map to the same canonical key, duplicate canonical | |
| keys are removed and later distinct keys shift forward. A top-k submission must | |
| therefore supply at least k distinct canonical guesses after mapping. Invalid, | |
| unmapped or blank predictions occupy the fixed denominator and score as wrong; | |
| rejection and structural non-support are also errors. | |
| ## Metrics | |
| - Primary: query-micro top-1 and species-macro top-1. | |
| - Secondary: query-micro and species-macro top-5 and top-20. | |
| - Paired comparisons: wrong-to-right, right-to-wrong and net-correct counts. | |
| - Uncertainty: 20,000 target-species-cluster bootstrap replicates. | |
| Query-micro weights every query equally. Species-macro first averages within | |
| each target taxon and then weights taxa equally. Scores are fractions in the | |
| JSON output; multiply by 100 for percent. | |
| ## Taxonomy mapping | |
| Outputs from another label system must be mapped to FishBase 25.04 before | |
| submission. Accepted names, synonyms and finer ranks that resolve to one species | |
| should map to one canonical key. If no defensible mapping exists, retain an | |
| unmapped token; do not drop the query. | |
| ## Reference results | |
| `reference-baselines.tsv` records frozen COVER-Fish configurations as reference | |
| baselines, not state of the art. The Fishial comparison in the paper is confined | |
| to its shared 775-name task and is not a full-roster QT26-QC baseline. | |
| ## Example | |
| ```bash | |
| python evaluation/score.py predictions.tsv --output metrics.json | |
| python evaluation/score.py new.tsv --compare old.tsv --output paired.json | |
| ``` | |