Independent reproduction of Decrop 2025 phytoplankton CNN metrics
None of substance. The reproduction uses the same code package (planktonclas), the same released model weights, and the same test.txt partition that the original evaluation reports against. One minor naming note: Table 1 of the paper lists test=33,829 and val=33,718, whereas the released split files have those numbers swapped (test.txt=33,718, val.txt=33,829). This study uses the released test.txt, which is the file the model was actually evaluated on. Hardware differs (CPU instead of GPU); CPU inference is exact arithmetic and does not change metric values.
The package planktonclas==0.2.0 can be installed from PyPI — the same code package authored by Decrop and colleagues and used to train the original model. The pretrained EfficientNetV2-B0 weights and the dataset split files are downloaded from Zenodo 10.5281/zenodo.15269453, and the FlowCam phytoplankton image dataset from Zenodo 10.5281/zenodo.10554845. Inference is run on the 33,718 images listed in the released test.txt using planktonclas.test_utils.predict, which applies 10-crop test-time augmentation. Top-1, top-5, micro F1, macro F1, and weighted F1 were computed via scikit-learn against the integer labels in test.txt.
This study reproduces the headline classification metrics reported by Decrop et al. 2025 for the EfficientNetV2-B0 phytoplankton classifier on the held-out test partition: top-1 accuracy, top-5 accuracy, and the macro/micro/weighted F1 scores. We use the authors' publicly released pretrained weights, the exact train/val/test split files distributed alongside the model, and the same 95-class taxonomy.
NP created using Declaring a replication study design according to FORRT