Fig. 3. Comparison of kit-level classification accuracy without adaptation (direct testing) and with adaptation.

a For the direct testing case, the model pre-trained on the base kit was directly applied on each of the new kit’s evaluation dataset. For the adaptation approach, the pre-trained model was adapted to each of the new kits, except for EcoTest housing 2 kit, using 10-shot adaptation (20 zone images) and the performance on their respective evaluation datasets is listed here. (The EcoTest housing 2 kit was identical in all aspects to the base kit expect for the housing, so the direct application of the base model without any adaptation was able to achieve 100% kit-level accuracies.) (n = 1 replicate per condition). b Images illustrating the challenge for few-shot learning. Sample images of challenging cases that were not classified correctly when using the base model without adaptation and were correctly classified using the adapted models. Shown are both false positives and false negatives (likely due to variations in colors and intensities of membrane background and bands).