dsh-deepseek-vision：为文本 DeepSeek 模型增加图片识别工具
dsh-deepseek-vision 是一款 MIT 许可证的 DSH 插件，旨在为仅支持文本的 DeepSeek 模型提供图片识别能力。该插件通过注册 `deepseek_vision` 工具，允许模型提交本地图片路径（支持 PNG、JPG、WebP、GIF，不支持 HEIC）及可选提示词。调用时，插件利用 DSH 子进程服务以数组参数方式安全调用本地 `deepseek-vision-cli`，将图片发送至 DeepSeek Web 端点进行识别，并将描述文本返回给模型。 用户需通过手动登录助手或 Token 登录建立 Web 会话，Token 仅保存在本地缓存。安装支持本地目录或 Re
Read Moredsh-vision-imagen：DeepSeek Harness 的视觉识别与图像生成插件
dsh-vision-imagen 是由 xuxun-oss 维护的 DeepSeek Harness（DSH）插件，旨在桥接 Google Gemini 的多模态能力，支持部分 OpenAI 兼容后端。它允许用户在常规 DeepSeek 会话中直接进行图片识别、文本生图及改图，无需手动切换模型。核心功能包括使用 `vision_read` 进行 OCR 和图像分析，`generate_image` 进行带自检迭代的文生图，以及 `edit_image` 进行图像优化。插件支持 Gemini 和 OpenAI 兼容后端（如 GPT-4o、Qwen-VL 等），具备自动降级机制。安装需 Node
Read Moredsh-mimo-vision-hint：零依赖提示 DSH 把图像识别分派给 opencode-go mimo-v2.5 子代理
dsh-mimo-vision-hint 是由 Isekai-Mfu 维护的零依赖 DSH profile bundle 插件（MIT 许可）。它不注册模型工具，也不提供 cordis 服务，仅通过注入系统提示，指导代理将图像识别任务分派给 opencode-go 的 mimo-v2.5 子代理；若 workflow 调用失败，则自动回退至 read_image。该插件适用于已配置 opencode-go provider、mimo-v2.5 模型及 workflow 工具的 DSH 部署环境。 安装时可通过命令 `dsh plugin --profile web add github:Ise
Read Morexiaoxianyu-office/dsh-image-tools：为 DeepSeek Harness 纯文本会话增加识图能力
`xiaoxianyu-office/dsh-image-tools` 是一款 DSH 插件，旨在为 `deepseek-v4-pro` 等纯文本模型赋予识图能力。该插件通过自动将聊天图片落盘至工作区、动态禁用原生 `read_image` 工具，并引导模型使用 `image_recognize` 工具委派视觉子 Agent（如 `xiaomi/mimo-v2.5`）来识别图像。它支持 `standard`、`code` 等多种预设会话，能根据当前模型能力动态判定拦截或桥接策略，并具备首次安装向导及故障自检功能。识图输出遵循严格规范，要求使用像素坐标、`#RRGGBB` 色值，并对图中未出现或
Read MoreDSH-Vision: A Plugin Adding Image Recognition Capabilities to DeepSeek Harness
dsh-vision is a DeepSeek Harness (DSH) bundle plugin maintained by Terry12138qy, licensed under the MIT License. This plugin is designed to address the issue that the DSH main model lacks native image recognition capabilities. By encapsulating the image recognition workflow, it sends local image paths or network URLs to Alibaba Cloud Bailian or Zhipu multimodal models, ultimately returning Chinese text descriptions for use by agents or Code Mode. Core features include automatically reading and encoding images, prioritizing the main model `qwen3.5-omni-plus`, and automatically switching to the fallback model `glm-4.6v-flash` for retries upon failures (e.g., quota exhaustion, rate limiting). It supports invocation via `await tools.vision(...)`.
Read MoreBuilding an Animal Recognition System with PaddlePaddle to Identify Thousands of Animal Species
This paper introduces a project for animal recognition using PaddlePaddle. Firstly, the animal recognition task can be completed with just a few lines of code. Secondly, a GUI interface is provided to facilitate users in uploading images for recognition. Finally, a Flask web interface is supported for Android calls, enabling cross - platform application. The project includes details such as model path, image reading, and prediction result output, and running screenshots are attached to demonstrate the implementation effect.
Read MoreEnd-to-End Recognition of Captchas Based on PaddlePaddle 2.0
Your code has covered most aspects of the CAPTCHA recognition project, including data processing, model training, and inference. Below are some suggestions for improvements and enhancements to your provided code: ### 1. Data Preprocessing Ensure the image dimensions are consistent (27x72), as this is the input size used during training. ### 2. Model Definition Your `Model` class has already encapsulated the network structure well. You can further optimize it and add more comments to facilitate understanding. ### 3. Training Process During the training process, ensure that when using multi-GPU training,
Read MoreImplementing Image Classification on Android Phones Based on TNN
This project is mainly an image classifier based on TensorFlow Lite, which can achieve real-time image recognition on Android devices. Its main functions and implementation steps are as follows: ### Project Structure - **MainActivity.java**: Implements gallery image selection and real-time camera prediction on the main interface. - **MNNClassification.java**: Integrates and encapsulates MNN model-related operations. ### Implementation Ideas 1. **Initialization**:
Read MoreImage Classification on Android Phones Based on MNN
This is a detailed guide on how to implement image classification in an Android application. You have successfully used TensorFlow Lite for image classification and demonstrated how to obtain input data through two methods: calling the camera and selecting images, and then passing this data to the model for prediction. ### Summary of Main Content 1. **Model Initialization**: First, load the pre-trained `mobilenet_v2_1.0_224.tflite` model and create a classifier instance. 2. **Reading Images and Pro
Read MoreImplementing Image Classification on Android Phones Based on Paddle Lite
Thank you for sharing this Android application development example for image classification based on Paddle Lite. Your project not only covers how to obtain categories from images but also introduces methods for real-time image recognition through the camera, enabling users to quickly understand information about the captured object in practical application scenarios. Below, I will further optimize and supplement the content you provided and offer some suggestions to improve the user experience or enhance code efficiency: ### 1. Project Structure and Resource Management Ensure the project has a clear file structure (e.g., `assets/image
Read MoreImplementation of Image Classification on Android Phones Based on TensorFlow Lite
This project mainly implements an image classification application based on TensorFlow Lite, which can perform object recognition using images from the camera or photo album on an Android device and provide real-time prediction functionality. The following is a detailed analysis of the core steps and key code of this project: ### Project Structure - **TFLiteModel**: Contains model-related configurations. - **MainActivity**: The main interface for launching the camera or selecting images for classification. - **RunClassifier** (Note: The original text seems to be incomplete here, so the translation preserves the placeholder as is.)
Read MoreCrowdNet: A Density Estimation Model Implemented with PaddlePaddle
That's the detailed tutorial on crowd flow density prediction. Through this project, you can learn how to use PaddlePaddle to solve practical problems, with detailed step-by-step guidance from training to prediction. If you encounter any issues or have any questions during the process, please feel free to ask in the comments section! We will also continuously pay attention to feedback to assist more friends who want to enter the AI field. We hope this case can help everyone better understand the process of data processing and model training.
Read MoreMy New Book Has Been Published!
This book "Deep Learning in Practice with PaddlePaddle" shares the author's experience from getting acquainted with PaddlePaddle to completing the book publication. It introduces the PaddlePaddle framework in detail and helps readers master practical applications through cases such as handwritten digit recognition. The content covers basic usage, dataset processing, object detection, as well as server-side and mobile-side applications. This book is suitable for machine learning enthusiasts and practitioners, and can also be used as a teaching reference. During the learning process of PaddlePaddle, the author shared tutorials through blogs, which ultimately led to the publication of this book.
Read MorePaddlePaddle From Beginner to "Alchemy" - Part 15: Deploying Prediction Models to Android Phones
Thank you for your sharing and detailed notes, which provide a great reference for developers who want to learn how to integrate PaddlePaddle for image recognition in Android applications. Below, I will summarize the information you provided and add some content that may help with understanding: ### 1. Environment Preparation - **Development Environment**: Ensure the latest version of Android Studio is installed. - **Permission Configuration**: Add necessary permissions in `AndroidManifest.xml`, such as read and write access to external storage.
Read More"PaddlePaddle From Beginner to Alchemist" - Part 11: Custom Image Dataset Recognition
This note mainly introduces how to use PaddlePaddle for training and prediction in image classification tasks, which specifically includes the following parts: ### 1. Dataset Preparation The author extracted 240 images from a dataset containing 6 categories of fruit images as the training set and organized them into CSV file format. ### 2. Model Construction A simple LeNet model structure was defined using PaddlePaddle. The model consists of two convolutional layers, two pooling layers, a fully connected layer, and finally performs classification through Softmax.
Read More"PaddlePaddle from Beginner to 'Alchemy' (Refined Version)" Part 4 - Convolutional Neural Networks
This tutorial provides a detailed introduction to training and predicting a handwritten digit recognition model using the PaddlePaddle framework. Below is a summary and further explanation of the key steps: ### 1. Preparing the Dataset First, the MNIST dataset is obtained from PaddlePaddle using the `fetch MNIST data` command. It is a widely used dataset for training machine learning models. ```python import paddle.v2 as paddle from paddle.v2.da ``` （Note: The code snippet appears truncated in the original input. The translation assumes the standard MNIST loading syntax in PaddlePaddle v2, though the full code may require additional imports or dataset initialization steps not visible in the provided snippet.)
Read MoreNotes on "My PaddlePaddle Learning Journey" ⑫ — Using the Visualization Tool VisualDL
This note provides a detailed introduction to how to use PaddlePaddle and VisualDL for the visualization of convolutional neural network (CNN) training. The following are the key points summarizing the content of the note: ### Visualizing CNN Training and Training Process with PaddlePaddle and VisualDL #### 1. Preparation - **Environment Installation**: Ensure that Python, PaddlePaddle, and VisualDL are installed. - **Dependency Library Import**: ```python
Read MoreNotes on "My PaddlePaddle Learning Journey" VII——End-to-End License Plate Recognition
This project mainly introduces how to use the PaddlePaddle framework to train a license plate recognition model. Below, I will summarize the key steps and concepts and provide some optimization suggestions. ### Summary of Key Steps 1. **Data Preparation**: - Collect and preprocess license plate images. - Create a label dictionary to map characters to indices. 2. **Model Construction**: - Use the PaddlePaddle framework to create an end-to-end recognition model. - The model includes an input layer, convolutional layers,
Read MoreNotes on "My Learning Journey with PaddlePaddle" – End-to-End Recognition of Verification Codes
This article provides a detailed introduction to the process of license plate recognition using PaddlePaddle, covering installation of the environment, reading the dataset, building the model, as well as training and testing. Below are summaries of several key points from the article: ### 1. Environment Setup The author first created a virtual environment for PaddlePaddle and configured the CUDA/CUDNN version. ### 2. Dataset Preparation A dataset containing a large number of license plate images was used. These data are publicly available on GitHub, and each license plate has a corresponding label. The author parsed the files
Read MoreNotes on My PaddlePaddle Learning Journey V——Captcha Recognition
Your tutorial provides a detailed introduction to using PaddlePaddle for captcha recognition, covering steps from dataset preparation, model design to final training and prediction. This series of steps is highly suitable for understanding and learning the basic processes and techniques of deep learning, especially its applications in the field of OCR (Optical Character Recognition). ### Code Structure Analysis 1. **Data Preprocessing**: - The `read_file` function is used to read image files and convert them into a format suitable for model input. - `load_and_tr
Read MoreNotes on "My PaddlePaddle Learning Journey" IV — Recognition of Custom Image Datasets
This series of notes mainly introduces how to implement a simple image recognition task using PaddlePaddle, including data preparation, model construction and training, as well as result prediction. The following is a summary of the main content of each part: ### 1. Environment Setup and Initial Configuration - **Environment Configuration**: First, install Python 3 and ensure it runs properly. - **Download Preprocessing Script**: Use the `DownloadImages.py` script to batch-download images to be recognized from Baidu Images. This script can perform downloads based on keywords (the original text is truncated here).
Read MoreNotes on "My Learning Journey with PaddlePaddle" III — CIFAR Color Image Recognition
This project is a CIFAR-10 image classification model implemented using PaddlePaddle, with a clear code structure and detailed comments. Below is a brief explanation of the main functions and principles of each part: ### 1. `vgg.py` This is a file containing the definition of the VGG network. VGG is a classic convolutional neural network architecture, and here it is implemented in PaddlePaddle. #### Main Content: - **Defines the VGG network structure**: Including multiple convolutional layers, pooling layers, and fully
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