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[2026-03-10] The Canopy Height Maps v2 (CHMv2) model and inference code are now available (more details on downloading the model weights and using the code here). The model weights are also available in Hugging Face Hub and supported by the Hugging Face Transformers library.

The largest collection of PyTorch image encoders / backbones.

txt inference code, following DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment.

> EdgeLM( ): small pre-trained models on edge/client devices

PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech and audio, with the state-of-art and influential models.

Datasets use the HDF5 format for fast loading. Download the data from HuggingFace and decompress with:

Self-supervised video pretraining has so far relied on architectural machinery to avoid representation collapse: an EMA target encoder, a stop-gradient, a capacity-limited predictor (V-JEPA), or pixel-space reconstruction with a dedicated decoder (VideoMAE).

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Related Codes: - Large language model evaluation: [llm-eval] - Large language model enhancement: [llm-enhance] - Robust machine learning: [robustlearn: robust machine learning] - Semi-supervised learning: [USB: unified semi-supervised learning benchmark] | [TorchSSL: a unified…

\ Cosmos straddles Renderer and Simulator. That ambiguity is the point. Before asking whether something is a world model, ask what it predicts and what you can do with the why-prediction-is-learning.