PyTorch implementation for EsViT (accepted in ICLR, 2022), built with two techniques:

A multi-stage Transformer architecture. Three multi-stage Transformer variants are implemented under the folder models.
A non-contrastive region-level matching pre-train task. The region-level matching task is implemented in function DDINOLoss(nn.Module) (Line 648) in main_esvit.py. Please use --use_dense_prediction True, otherwise only the view-level task is used.

Figure: Efficiency vs accuracy comparison under the linear classification protocol on ImageNet. Left: Throughput of all SoTA SSL vision systems, circle sizes indicates model parameter counts; Right: performance over varied parameter counts for models with moderate (throughout/#parameters) ratio. Please refer Section 4.1 for details.

Updates

[08/19/2022] Organizing ECCV Workshop Computer Vision in the Wild (CVinW), where two challenges are hosted to evaluate the zero-shot, few-shot and full-shot performance of pre-trained vision models in downstream tasks:
- ``Image Classification in the Wild (ICinW)'' Challenge evaluates on 20 image classification tasks.
- ``Object Detection in the Wild (ODinW)'' Challenge evaluates on 35 object detection tasks.