[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9745":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":43,"readmeContent":44,"aiSummary":45,"trendingCount":16,"starSnapshotCount":16,"syncStatus":46,"lastSyncTime":47,"discoverSource":48},9745,"ImageAI","OlafenwaMoses\u002FImageAI","OlafenwaMoses","A python library built to empower developers to build applications and systems  with self-contained Computer Vision capabilities","https:\u002F\u002Fwww.genxr.co\u002F#products",null,"Python",8868,2195,281,294,0,9,41,"MIT License",false,"master",true,[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"ai-practice-recommendations","algorithm","artificial-intelligence","artificial-neural-networks","densenet","detection","gpu","image-prediction","image-recognition","imageai","inceptionv3","machine-learning","object-detection","offline-capable","prediction","python","python3","squeezenet","video","2026-06-12 02:02:12","# ImageAI (v3.0.3)\n\n\n\n[![Build Status](https:\u002F\u002Ftravis-ci.com\u002FOlafenwaMoses\u002FImageAI.svg?branch=master)](https:\u002F\u002Ftravis-ci.com\u002FOlafenwaMoses\u002FImageAI)  [![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-yellow.svg)](https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FImageAI\u002Fblob\u002Fmaster\u002FLICENSE) [![PyPI version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fimageai.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fimageai)   [![Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fimageai\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fimageai) [![Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fimageai\u002Fweek)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fimageai)\n\nAn open-source python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code.\n \n If you will like to sponsor this project, kindly visit the \u003Cstrong>[Github sponsor page](https:\u002F\u002Fgithub.com\u002Fsponsors\u002FOlafenwaMoses)\u003C\u002Fstrong>.\n \n \n## ---------------------------------------------------\n## Introducing Jarvis and TheiaEngine.\n\nWe the creators of ImageAI are glad to announce 2 new AI projects to provide state-of-the-art Generative AI, LLM and Image Understanding on your personal computer and servers. \n\n\n[![](jarvis.png)](https:\u002F\u002Fjarvis.genxr.co)\n\nInstall Jarvis on PC\u002FMac to setup limitless access to LLM powered AI Chats for your every day work, research and generative AI needs with 100% privacy and full offline capability.\n\n\nVisit [https:\u002F\u002Fjarvis.genxr.co](https:\u002F\u002Fjarvis.genxr.co\u002F) to get started.\n\n\n[![](theiaengine.png)](https:\u002F\u002Fwww.genxr.co\u002Ftheia-engine)\n\n\n[TheiaEngine](https:\u002F\u002Fwww.genxr.co\u002Ftheia-engine), the next-generation computer Vision AI API capable of all Generative and Understanding computer vision tasks in a single API call and available via REST API to all programming languages. Features include\n- **Detect 300+ objects** ( 220 more objects than ImageAI)\n- **Provide answers to any content or context questions** asked on an image\n  - very useful to get information on any object, action or information without needing to train a new custom model for every tasks\n-  **Generate scene description and summary**\n-  **Convert 2D image to 3D pointcloud and triangular mesh**\n-  **Semantic Scene mapping of objects, walls, floors, etc**\n-  **Stateless Face recognition and emotion detection**\n-  **Image generation and augmentation from prompt**\n-  etc.\n\nVisit [https:\u002F\u002Fwww.genxr.co\u002Ftheia-engine](https:\u002F\u002Fwww.genxr.co\u002Ftheia-engine) to try the demo and join in the beta testing today.\n## ---------------------------------------------------\n \n![](logo1.png)\n\nDeveloped and maintained by [Moses Olafenwa](https:\u002F\u002Ftwitter.com\u002FOlafenwaMoses)\n\n---\n\nBuilt with simplicity in mind, **ImageAI** \n    supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking\n    and image predictions trainings. **ImageAI** currently supports image prediction and training using 4 different Machine Learning algorithms \n    trained on the ImageNet-1000 dataset. **ImageAI** also supports object detection, video detection and object tracking  using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Finally, **ImageAI** allows you to train custom models for performing detection and recognition of new objects. \n   \nEventually, **ImageAI** will provide support for a wider and more specialized aspects of Computer Vision\n\n\n**New Release : ImageAI 3.0.2**\n\nWhat's new:\n- PyTorch backend\n- TinyYOLOv3 model training\n\n\n### TABLE OF CONTENTS\n- \u003Ca href=\"#installation\" > :white_square_button: Installation\u003C\u002Fa>\n- \u003Ca href=\"#features\" > :white_square_button: Features\u003C\u002Fa>\n- \u003Ca href=\"#documentation\" > :white_square_button: Documentation\u003C\u002Fa>\n- \u003Ca href=\"#sponsors\" > :white_square_button: Sponsors\u003C\u002Fa>\n- \u003Ca href=\"#sample\" > :white_square_button: Projects Built on ImageAI\u003C\u002Fa>\n- \u003Ca href=\"#real-time-and-high-performance-implementation\" > :white_square_button: High Performance Implementation\u003C\u002Fa>\n- \u003Ca href=\"#recommendation\" > :white_square_button: AI Practice Recommendations\u003C\u002Fa>\n- \u003Ca href=\"#contact\" > :white_square_button: Contact Developers\u003C\u002Fa>\n- \u003Ca href=\"#citation\" > :white_square_button: Citation\u003C\u002Fa>\n- \u003Ca href=\"#ref\" > :white_square_button: References\u003C\u002Fa>\n\n\n\n## Installation\n\u003Cdiv id=\"installation\">\u003C\u002Fdiv>\n \nTo install ImageAI, run the python installation instruction below in the command line:\n\n- [Download and Install](https:\u002F\u002Fwww.python.org\u002Fdownloads\u002F) **Python 3.7**, **Python 3.8**, **Python 3.9** or **Python 3.10**\n- Install dependencies\n  - **CPU**: Download [requirements.txt](https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FImageAI\u002Fblob\u002Fmaster\u002Frequirements.txt) file and install via the command\n    ```\n    pip install -r requirements.txt\n    ```\n    or simply copy and run the command below\n\n    ```\n    pip install cython pillow>=7.0.0 numpy>=1.18.1 opencv-python>=4.1.2 torch>=1.9.0 --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcpu torchvision>=0.10.0 --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcpu pytest==7.1.3 tqdm==4.64.1 scipy>=1.7.3 matplotlib>=3.4.3 mock==4.0.3\n    ```\n\n  - **GPU\u002FCUDA**: Download [requirements_gpu.txt](https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FImageAI\u002Fblob\u002Fmaster\u002Frequirements_gpu.txt) file and install via the command\n    ```\n    pip install -r requirements_gpu.txt\n    ```\n    or smiply copy and run the command below\n    ```\n    pip install cython pillow>=7.0.0 numpy>=1.18.1 opencv-python>=4.1.2 torch>=1.9.0 --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu102 torchvision>=0.10.0 --extra-index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu102 pytest==7.1.3 tqdm==4.64.1 scipy>=1.7.3 matplotlib>=3.4.3 mock==4.0.3\n    ```\n- If you plan to train custom AI models, download [requirements_extra.txt](https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FImageAI\u002Fblob\u002Fmaster\u002Frequirements_extra.txt) file and install via the command\n  \n  ```\n  pip install -r requirements_extra.txt\n  ```\n  or simply copy and run the command below\n  ```\n  pip install pycocotools@git+https:\u002F\u002Fgithub.com\u002Fgautamchitnis\u002Fcocoapi.git@cocodataset-master#subdirectory=PythonAPI\n  ```\n- Then run the command below to install ImageAI\n  ```\n  pip install imageai --upgrade\n  ```\n\n## Features\n\u003Cdiv id=\"features\">\u003C\u002Fdiv>\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Image Classification\u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\u003Cimg src=\"data-images\u002F1.jpg\" >\n    \u003Ch4>ImageAI provides 4 different algorithms and model types to perform image prediction, trained on the ImageNet-1000 dataset. The 4 algorithms provided for image prediction include MobileNetV2, ResNet50, InceptionV3 and DenseNet121.\n    Click the link below to see the full sample codes, explanations and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FClassification\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n \u003Cdiv id=\"features\">\u003C\u002Fdiv>\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Object Detection \u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n        \u003Cimg src=\"data-images\u002Fimage2new.jpg\">\n        \u003Ch4>ImageAI provides very convenient and powerful methods to perform object detection on images and extract each object from the image. The object detection class provides support for RetinaNet, YOLOv3 and TinyYOLOv3, with options to adjust for state of the art performance or real time processing. Click the link below to see the full sample codes, explanations and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FDetection\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Video Object Detection & Analysis\u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\u003Cimg src=\"data-images\u002Fvideo_analysis_visualization.jpg\">\n    \u003Ch4>ImageAI provides very convenient and powerful methods to perform object detection in videos. The video object detection class provided only supports the current state-of-the-art RetinaNet. Click the link to see the full videos, sample codes, explanations and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FDetection\u002FVIDEO.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n\n \u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Custom Classification model training \u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n        \u003Cimg src=\"data-images\u002Fidenprof.jpg\">\n        \u003Ch4>ImageAI provides classes and methods for you to train a new model that can be used to perform prediction on your own custom objects. You can train your custom models using MobileNetV2, ResNet50, InceptionV3 and DenseNet in 5 lines of code. Click the link below to see the guide to preparing training images, sample training codes, explanations and best practices.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FClassification\u002FCUSTOMTRAINING.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n \u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Custom Model Classification\u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\u003Cimg src=\"data-images\u002F4.jpg\">\n    \u003Ch4>ImageAI provides classes and methods for you to run image prediction your own custom objects using your own model trained with ImageAI Model Training class. You can use your custom models trained with MobileNetV2, ResNet50, InceptionV3 and DenseNet and the JSON file containing the mapping of the custom object names. Click the link below to see the guide to sample training codes, explanations, and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FClassification\u002FCUSTOMCLASSIFICATION.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n \u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Custom Detection Model Training \u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n        \u003Cimg src=\"data-images\u002Fheadsets.jpg\">\n        \u003Ch4>ImageAI provides classes and methods for you to train new YOLOv3 or TinyYOLOv3 object detection models on your custom dataset. This means you can train a model to detect literally any object of interest by providing the images, the annotations and training with ImageAI. Click the link below to see the guide to sample training codes, explanations, and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FDetection\u002FCustom\u002FCUSTOMDETECTIONTRAINING.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \n \u003C\u002Ftable>\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Custom Object Detection\u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\u003Cimg src=\"data-images\u002Fholo2-detected.jpg\">\n    \u003Ch4>ImageAI now provides classes and methods for you detect and recognize your own custom objects in images using your own model trained with the DetectionModelTrainer class. You can use your custom trained YOLOv3 or TinyYOLOv3 model and the **.json** file generated during the training. Click the link below to see the guide to sample training codes, explanations, and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FDetection\u002FCustom\u002FCUSTOMDETECTION.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n \u003C\u002Ftable>\n\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd>\u003Ch2> Custom Video Object Detection & Analysis \u003C\u002Fh2> \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n        \u003Cimg src=\"data-images\u002Fcustomvideodetection.gif\">\n        \u003Ch4>ImageAI now provides classes and methods for you detect and recognize your own custom objects in images using your own model trained with the DetectionModelTrainer class. You can use your custom trained YOLOv3 or TinyYOLOv3 model and the **.json** file generated during the training. Click the link below to see the guide to sample training codes, explanations, and best practices guide.\u003C\u002Fh4>\n    \u003Ca href=\"imageai\u002FDetection\u002FCustom\u002FCUSTOMVIDEODETECTION.md\"> >>> Get Started\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n \u003C\u002Ftable>\n\n## Documentation\n\u003Cdiv id=\"documentation\">\u003C\u002Fdiv>\n\nWe have provided full documentation for all **ImageAI** classes and functions. Visit the link below:\n\n- Documentation - **English Version**  [https:\u002F\u002Fimageai.readthedocs.io](https:\u002F\u002Fimageai.readthedocs.io)\n\n\n## Sponsors\n\u003Cdiv id=\"sponsors\">\u003C\u002Fdiv>\n\n\n## Real-Time and High Performance Implementation\n\u003Cdiv id=\"performance\">\u003C\u002Fdiv>\n\n**ImageAI** provides abstracted and convenient implementations of state-of-the-art Computer Vision technologies. All of **ImageAI** implementations and code can work on any computer system with moderate CPU capacity. However, the speed of processing for operations like image prediction, object detection and others on CPU is slow and not suitable for real-time applications. To perform real-time Computer Vision operations with high performance, you need to use GPU enabled technologies.\n\n**ImageAI** uses the PyTorch backbone for it's Computer Vision operations. PyTorch supports both CPUs and GPUs ( Specifically NVIDIA GPUs.  You can get one for your PC or get a PC that has one) for machine learning and artificial intelligence algorithms' implementations.\n\n\n\n## Projects Built on ImageAI\n\u003Cdiv id=\"sample\">\u003C\u002Fdiv>\n\n\n\n## AI Practice Recommendations\n\u003Cdiv id=\"recommendation\">\u003C\u002Fdiv>\n\nFor anyone interested in building AI systems and using them for business, economic,  social and research purposes, it is critical that the person knows the likely positive, negative and unprecedented impacts the use of such technologies will have.\nThey must also be aware of approaches and practices recommended by experienced industry experts to ensure every use of AI brings overall benefit to mankind.\nWe therefore recommend to everyone that wishes to use ImageAI and other AI tools and resources to read Microsoft's January 2018 publication on AI titled \"The Future Computed : Artificial Intelligence and its role in society\".\nKindly follow the link below to download the publication.\n\n[https:\u002F\u002Fblogs.microsoft.com\u002Fblog\u002F2018\u002F01\u002F17\u002Ffuture-computed-artificial-intelligence-role-society](https:\u002F\u002Fblogs.microsoft.com\u002Fblog\u002F2018\u002F01\u002F17\u002Ffuture-computed-artificial-intelligence-role-society\u002F)\n\n### Contact Developer\n\u003Cdiv id=\"contact\">\u003C\u002Fdiv>\n\n- **Moses Olafenwa**\n    * _Email:_ guymodscientist@gmail.com\n    * _Twitter:_ [@OlafenwaMoses](https:\u002F\u002Ftwitter.com\u002FOlafenwaMoses)\n    * _Medium:_ [@guymodscientist](https:\u002F\u002Fmedium.com\u002F@guymodscientist)\n    * _Facebook:_ [moses.olafenwa](https:\u002F\u002Ffacebook.com\u002Fmoses.olafenwa)\n- **John Olafenwa**\n    * _Email:_ johnolafenwa@gmail.com\n    * _Website:_ [https:\u002F\u002Fjohn.aicommons.science](https:\u002F\u002Fjohn.aicommons.science)\n    * _Twitter:_ [@johnolafenwa](https:\u002F\u002Ftwitter.com\u002Fjohnolafenwa)\n    * _Medium:_ [@johnolafenwa](https:\u002F\u002Fmedium.com\u002F@johnolafenwa)\n    * _Facebook:_ [olafenwajohn](https:\u002F\u002Ffacebook.com\u002Folafenwajohn)\n\n\n### Citation\n\u003Cdiv id=\"citation\">\u003C\u002Fdiv>\n\nYou can cite **ImageAI** in your projects and research papers via the **BibTeX** entry below.  \n  \n```\n@misc {ImageAI,\n    author = \"Moses\",\n    title  = \"ImageAI, an open source python library built to empower developers to build applications and systems  with self-contained Computer Vision capabilities\",\n    url    = \"https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FImageAI\",\n    month  = \"mar\",\n    year   = \"2018--\"\n}\n```\n\n\n\n ### References\n \u003Cdiv id=\"ref\">\u003C\u002Fdiv>\n\n 1. Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras\n[https:\u002F\u002Fgithub.com\u002Ftitu1994\u002FDenseNet](https:\u002F\u002Fgithub.com\u002Ftitu1994\u002FDenseNet)\n 2. Broad Institute of MIT and Harvard, Keras package for deep residual networks\n[https:\u002F\u002Fgithub.com\u002Fbroadinstitute\u002Fkeras-resnet](https:\u002F\u002Fgithub.com\u002Fbroadinstitute\u002Fkeras-resnet)\n 3. Fizyr, Keras implementation of RetinaNet object detection\n[https:\u002F\u002Fgithub.com\u002Ffizyr\u002Fkeras-retinanet](https:\u002F\u002Fgithub.com\u002Ffizyr\u002Fkeras-retinanet)\n 4. Francois Chollet, Keras code and weights files for popular deeplearning models\n[https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-models](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-models)\n 5. Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \u003C0.5MB model size\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.07360](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.07360)\n 6. Kaiming H. et al, Deep Residual Learning for Image Recognition\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.03385](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.03385)\n 7. Szegedy. et al, Rethinking the Inception Architecture for Computer Vision\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.00567](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.00567)\n 8. Gao. et al, Densely Connected Convolutional Networks\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.06993](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.06993)\n 9. Tsung-Yi. et al, Focal Loss for Dense Object Detection\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02002](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02002)\n 10. O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1409.0575](https:\u002F\u002Farxiv.org\u002Fabs\u002F1409.0575)\n 11. TY Lin et al, Microsoft COCO: Common Objects in Context\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1405.0312](https:\u002F\u002Farxiv.org\u002Fabs\u002F1405.0312)\n 12. Moses & John Olafenwa, A collection of images of identifiable professionals.\n[https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FIdenProf](https:\u002F\u002Fgithub.com\u002FOlafenwaMoses\u002FIdenProf)\n 13. Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement.\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02767](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02767)\n 14. Experiencor, Training and Detecting Objects with YOLO3\n[https:\u002F\u002Fgithub.com\u002Fexperiencor\u002Fkeras-yolo3](https:\u002F\u002Fgithub.com\u002Fexperiencor\u002Fkeras-yolo3)\n 15. MobileNetV2: Inverted Residuals and Linear Bottlenecks\n[https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04381](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04381)\n 16. YOLOv3 in PyTorch > ONNX > CoreML > TFLite [https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3)\n","ImageAI 是一个开源的 Python 库，旨在通过几行简单的代码帮助开发者构建具备自包含深度学习和计算机视觉能力的应用程序和系统。其核心功能包括图像预测、自定义图像预测、物体检测、视频检测、视频对象跟踪及图像预测训练等，支持使用多种机器学习算法（如 InceptionV3、DenseNet 和 SqueezeNet）进行图像识别与处理。该库特别适合需要快速集成高效图像分析功能但又希望减少开发复杂度的场景，例如安防监控、自动驾驶辅助系统或是任何涉及大规模图像数据处理的应用领域。",2,"2026-06-11 03:24:31","top_topic"]