ImageNet

WordNet and synsets

Each class corresponds to a synset ID (e.g. n01440764 “tench”). Hierarchical relations in WordNet are not always reflected in flat 1-of-K training—hierarchical loss is optional research direction.

ILSVRC tasks

Beyond single-label classification, historical challenges included localization (bbox) and detection. Today, COCO is more common for detection benchmarks, while ImageNet-pretrained backbones remain the default initialization.

Using label names in code

from torchvision.models import resnet50, ResNet50_Weights

w = ResNet50_Weights.IMAGENET1K_V2
names = w.meta["categories"]  # list of 1000 strings
# idx = logits.argmax(dim=1).item()
# print(names[idx])

Different weight versions may share the same 1k label order—confirm in the weights metadata you load.

Transfer learning

Replace the classifier head, freeze early layers optionally, train on your domain. Features are biased toward ImageNet objects; medical or industrial imagery may need more adaptation or different pretraining (SimCLR, CLIP, domain-specific data).