Search Results for "image segmentation delphi source code"
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Showing 15 open source projects for "image segmentation delphi source code"
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Sa2VA
Official Repo For "Sa2VA: Marrying SAM2 with LLaVA
Sa2VA is a cutting-edge open-source multi-modal large language model (MLLM) developed by ByteDance that unifies dense segmentation, visual understanding, and language-based reasoning across both images and videos. It merges the segmentation power of a state-of-the-art video segmentation model (based on SAM‑2) with the vision-language reasoning capabilities of a strong LLM backbone (derived from models like InternVL2.5 / Qwen-VL series), yielding a system that can answer questions about... -
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Qwen-MM-Plugins
Make any agent harness multimodal-native
Qwen-MM-Plugins is a collection of native multimodal capabilities that can extend Qwen-powered agent harnesses beyond text-only workflows. Each capability combines an instructional skill with an optional MCP server that exposes specialized tools. The core package can inspect images, videos, documents, and 3D models while providing OCR, grounding, segmentation, transcription, and visual question answering. Additional modules support long-video memory, audio-video analysis, media generation,... -
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CleanVision
Automatically find issues in image datasets
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset! The quality of machine learning models hinges on the quality of the data used... -
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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time... -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval,... -
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Depth Anything 3
Recovering the Visual Space from Any Views
Depth Anything 3 is a research-driven project that brings accurate and dense depth estimation to any input image or video, enabling foundational understanding of 3D structure from 2D visual content. Designed to work across diverse scenes, lighting conditions, and image types, it uses advanced neural networks trained on large, heterogeneous datasets, producing depth maps that reveal scene depth relationships and object surfaces with strong fidelity. The model can be applied to photography,... -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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TaskMatrix
Enable sending and receiving images during chatting
TaskMatrix is an experimental AI ecosystem designed to connect large language models with visual foundation models, APIs, and external systems in order to complete multimodal tasks collaboratively. The project expands beyond traditional chatbot behavior by enabling AI systems to process, generate, edit, and reason about images while coordinating multiple specialized models simultaneously. Originally introduced alongside the Visual ChatGPT concept, TaskMatrix acts as an orchestration... -
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Unet
Source code for unet-pytorch, which can train its own model
Unet-pytorch is a PyTorch implementation of U-Net for semantic segmentation workflows. The repository is built around training, prediction, and mIoU evaluation for VOC-style segmentation data and medical-style datasets. It includes scripts for general training, medical dataset training, prediction, annotation handling, model summaries, and evaluation. The project supports multiple backbones, data processing utilities, extensive comments, and adjustable training parameters. Its README notes... -
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pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks
pytorch-fcn is a PyTorch implementation of Fully Convolutional Networks for semantic image segmentation. It reproduces the influential FCN approach that converts classification networks into dense, pixel-level predictors. The package includes FCN32s, FCN16s, FCN8s, and FCN8s-at-once variants with progressively finer output reconstruction. Training code and a PASCAL VOC example are provided so users can reproduce baseline experiments. -
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deep-learning-for-image-processing
deep learning for image processing including classification
deep-learning-for-image-processing is an extensive educational repository covering practical deep learning methods for computer vision. It organizes implementations, explanations, presentation files, and video lessons around major neural network architectures. The material teaches both model structure and training workflows, with examples built in PyTorch and TensorFlow through Keras. Classification topics range from LeNet and AlexNet to ResNet, EfficientNet, Vision Transformer, Swin... -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on... -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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Cyberbit
Visual Cryptography and Bit-Plane Complexity Segmentation in Python
Hide secret messages in your pictures! Cyberbit lets you transmit a secret message to an intended person in a new manner. The secret message is an 1-bit deep black & white image. The message can contain written text, a drawing, a secret code etc. Using Visual Cryptography the application splits the secret message into two "shadow images" called <<shares>>. Any of the two shares will be embedded into a "normal" image file called by tradition <<vessel>>. You can use a method of hand to... -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
... - Monk Object Detection - https://github.com/Tessellate-Imaging/Monk_Object_Detection. Monk object detection is our take on assembling state of the art object detection, image segmentation, pose estimation algorithms at one place, making them low code and easily configurable on any machine. - Monk GUI - https://github.com/Tessellate-Imaging/Monk_Gui. An interface over these low code tools for non coders.
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