Datasets

In this study, datasets from two fundamentally different real-world ecological use cases were employed. The objects of interest in these images were manually counted in previous studies^2,8,36,37, without the aim of DL applications.

The first dataset consists of 3585 microscopic images of otoliths (i.e., hearing stones) of plaice (Pleuronectes platessa). Newly settled juvenile plaice of various length classes were collected at stations along the North Sea and Wadden Sea coast during 23 sampling campaigns conducted over 6 years. Each individual fish was measured, the sagittal otoliths were removed and microscopic images of two zoom levels (10×20 and 10×10, depending on fish length) were made. Post-settlement daily growth rings outside the accessory growth centre were then counted by eye^6,7. In this dataset, images of otoliths with less than 16 and more than 45 rings were scarce (Fig. 6). Therefore, a stratified random design was used to select 120 images to evaluate the model performance over the full range of ring counts: all 3585 images were grouped in eight bins according to their label (Fig. 6) and from each bin 15 images were randomly selected for the test set. Out of the remaining 3465 images, 80% of the images were randomly selected for training and 20% were used as a validation set, which is used to estimate the model performance and optimise hyperparameters during training.

Distribution of the labels (i.e., number of post-settlement rings) of all images in the otolith dataset (n=3585).

The second dataset consists of 11,087 aerial images (named ‘main dataset’ from now onwards) of hauled out grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina), collected between 2005 and 2019 in the Dutch part of the Wadden Sea^2,36. Surveys for both species were performed multiple times each year: approximately three times during pupping season and twice during the moult⁸. During these periods, seals haul out on land in larger numbers. Images were taken manually through the airplane window whenever seals were sighted, while flying at a fixed height of approximately 150m, using different focal lengths (80-400mm). Due to variations in survey conditions (e.g., weather, lighting) and image composition (e.g., angle of view, distance towards seals), this main dataset is highly variable. Noisy labels further complicated the use of this dataset: seals present in multiple (partially) overlapping images were counted only once, and were therefore not included in the count label of each image. Recounting the seals on all images in this dataset to deal with these noisy labels would be a tedious task, compromising one of the main aims of this study of reducing annotation efforts. Instead, only a selection of the main dataset was recounted and used for training and testing. First, 100 images were randomly selected (and recounted) for the test set. In the main dataset, images with a high number of seals were scarce, while images with a low number of seals were abundant (Fig. 7, panel A). Therefore, as with the otoliths, all 11,087 images were grouped into 20 bins according to their label (Fig. 7, panel A), after which five images were randomly selected from each bin for the test set. Second, images of sufficient quality and containing easily identifiable were selected from the main dataset (and recounted) for training and validation, until 787 images were retained (named ‘seal subset 1’). In order to create images with zero seals (i.e., just containing the background) and to remove seals that are only partly photographed along the image borders, some of these images were cropped. The dimensions of those cropped images were preserved and, if required, the image-level annotation was modified accordingly. The resulting ‘seal subset 1’ only contains images with zero to 99 seals (Fig. 7, panel B). These 787 images were then randomly split in a training (80%) and validation set (20%). In order to still take advantage of the remaining 10,200 images from the main dataset, a two-step label refinement was performed (see the section “Dealing with noisy labels: two-step label refinement” below).

Distribution of the labels (i.e., number of seals) in (A) the seal main dataset (n=11,087), (B) ‘seal subset 1’ (n=787) and (C) ‘seal subset 2’ (n=100).