imgclsmob is queued for analysis. Crawling candidates and ranking them takes a few minutes. This page updates itself when the list exists — you can also come back later.
What is known so far comes from the other direction:
These projects were analysed and named imgclsmob among their alternatives. The relationship is not symmetric — how imgclsmob rates them is a separate judgement, made when imgclsmob is analysed in its own right.
calls imgclsmob “Use if you need a broader pre-built model zoo covering classification, segmentation, and detection without video explanations.”
deep learning for image processing including classification and object-detection etc.
calls imgclsmob “Stale multi-framework image classification model zoo; choose if you need Keras/TFLite models, not PyTorch-only upkeep.”
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
calls imgclsmob “choose for a sandbox of cross-framework image classification models, but stale and narrower.”
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.