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Reproducing cross-domain few-shot benchmark on Sentinel-2 with matching architecture
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We reimplemented the ResNet-10 architecture from Guo et al.'s published code rather than running their original codebase (which requires Python 3.5 and PyTorch 0.4). Our implementation uses PyTorch 2.11. Minor differences in random seed and data loading may exist.
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We built a ResNet-10 backbone (4.9 million parameters) matching Guo et al.'s custom implementation: SimpleBlock residual blocks, custom weight initialization, 7×7 average pooling for 224×224 pixel input images. Training used 40,000 episodic steps on mini-ImageNet (38,400 photographs of everyday objects) with data augmentation (random resized crop, colour jitter, horizontal flip). Evaluation on EuroSAT (27,000 real Sentinel-2 satellite patches, 10 land cover types) over 200 random 5-way tasks with 5, 20, and 50 labeled examples.
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Reproducing the exact Prototypical Networks results from Guo et al. (2020) Table 1 on EuroSAT satellite imagery, matching their architecture, image resolution, and training procedure.
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Anne Fouilloux
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NP created using Declaring a replication study design according to FORRT
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