Global

简体中文 · Configuration · Troubleshooting · Security · 🔍 ModSearch (the best free web search plugin for DSH)

[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.

0, running FP8 on the tensor cores. The intermediates match too, not just the final image: all 75 block boundaries, byte for byte.

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

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

This work presents Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from arbitrary visual inputs, with or without known camera poses.

deep learning for image processing including classification and object-detection etc.

Implementation of Vision Transformer , a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. Significance is further explained in Yannic Kilcher's video.

pix2tex: Using a ViT to convert images of equations into LaTeX code.

The models were pre-trained on the ImageNet and ImageNet-21k datasets. We provide the code for fine-tuning the released models in JAX/Flax.

docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning. Ongoing development and maintenance by t2k.

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

This repository contains demos I made with the Transformers library by HuggingFace.

inter-ocular NME %, lower is better; CPU latency on an i9-10900K with onnxruntime 1.22 CPU EP, batch 1.

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).

PointDiT is a minimalist pixel-space Diffusion Transformer for monocular geometry estimation.