mobilenetv3_small_100.lamb_in1k and vit-base-patch16-224 are both image-classification models. See each entry for specifics.
MobileNetV3 small model at 100% width multiplier, trained on ImageNet-1k using the LAMB optimizer via the timm library. At under 3M parameters, it targets image classification on mobile and edge hardware where latency and memory are primary constraints. Part of timm's standardized pretrained model zoo with consistent preprocessing and inference APIs.
Google's ViT-Base (Vision Transformer base model) with 16×16 pixel patch size trained at 224px resolution on ImageNet-21k and fine-tuned on ImageNet-1k. The paper introducing ViTs demonstrated that pure transformer architectures without convolutional inductive bias can match CNNs on image classification when trained on sufficient data. Widely used as a starting backbone for image classification fine-tuning.
Pick based on your compute budget and specific task requirements.