{"id":13687165,"url":"https://github.com/fangwei123456/spikingjelly","last_synced_at":"2025-05-13T20:09:37.704Z","repository":{"id":37783374,"uuid":"228557692","full_name":"fangwei123456/spikingjelly","owner":"fangwei123456","description":"SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.","archived":false,"fork":false,"pushed_at":"2025-05-10T14:02:49.000Z","size":44448,"stargazers_count":1591,"open_issues_count":162,"forks_count":267,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-05-10T15:19:35.276Z","etag":null,"topics":["deep-learning","dvs","machine-learning","pytorch","snn","spiking-neural-networks"],"latest_commit_sha":null,"homepage":"https://spikingjelly.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fangwei123456.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-12-17T07:23:55.000Z","updated_at":"2025-05-10T14:40:41.000Z","dependencies_parsed_at":"2024-01-14T18:20:26.790Z","dependency_job_id":"d70d17f7-3a67-4356-b245-a679854be668","html_url":"https://github.com/fangwei123456/spikingjelly","commit_stats":{"total_commits":1809,"total_committers":32,"mean_commits":56.53125,"dds":"0.23438363736871204","last_synced_commit":"214a8479623f29a265d713ad04b38effdd0f66e9"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fangwei123456%2Fspikingjelly","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fangwei123456%2Fspikingjelly/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fangwei123456%2Fspikingjelly/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fangwei123456%2Fspikingjelly/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fangwei123456","download_url":"https://codeload.github.com/fangwei123456/spikingjelly/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253434139,"owners_count":21907858,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","dvs","machine-learning","pytorch","snn","spiking-neural-networks"],"created_at":"2024-08-02T15:00:49.799Z","updated_at":"2025-05-13T20:09:37.661Z","avatar_url":"https://github.com/fangwei123456.png","language":"Python","funding_links":[],"categories":["Spiking and Neuromorphic Frameworks","Frameworks","Repositories :open_file_folder:"],"sub_categories":["Neuromorphic hardware"],"readme":"# SpikingJelly\n![GitHub last commit](https://img.shields.io/github/last-commit/fangwei123456/spikingjelly) \n[![Documentation Status](https://readthedocs.org/projects/spikingjelly/badge/?version=latest)](https://spikingjelly.readthedocs.io/zh_CN/latest) \n[![PyPI](https://img.shields.io/pypi/v/spikingjelly)](https://pypi.org/project/spikingjelly) \n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spikingjelly)](https://pypi.org/project/spikingjelly) \n![repo size](https://img.shields.io/github/repo-size/fangwei123456/spikingjelly)\n![GitHub issues](https://img.shields.io/github/issues/fangwei123456/spikingjelly)\n![GitHub closed issues](https://img.shields.io/github/issues-closed-raw/fangwei123456/spikingjelly)\n![GitHub pull requests](https://img.shields.io/github/issues-pr/fangwei123456/spikingjelly)\n![GitHub closed pull requests](https://img.shields.io/github/issues-pr-closed/fangwei123456/spikingjelly)\n![Visitors](https://api.visitorbadge.io/api/visitors?path=fangwei123456%2Fspikingjelly%20\u0026countColor=%23263759\u0026style=flat)\n![GitHub forks](https://img.shields.io/github/forks/fangwei123456/spikingjelly)\n![GitHub Repo stars](https://img.shields.io/github/stars/fangwei123456/spikingjelly)\n![GitHub contributors](https://img.shields.io/github/contributors/fangwei123456/spikingjelly)\n\n\nEnglish | [中文(Chinese)](./README_cn.md)\n\n![demo](./docs/source/_static/logo/demo.png)\n\nSpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on [PyTorch](https://pytorch.org/).\n\nThe documentation of SpikingJelly is written in both English and Chinese: https://spikingjelly.readthedocs.io.\n\n- [Installation](#installation)\n- [Build SNN In An Unprecedented Simple Way](#build-snn-in-an-unprecedented-simple-way)\n- [Fast And Handy ANN-SNN Conversion](#fast-and-handy-ann-snn-conversion)\n- [CUDA-Enhanced Neuron](#cuda-enhanced-neuron)\n- [Device Supports](#device-supports)\n- [Neuromorphic Datasets Supports](#neuromorphic-datasets-supports)\n- [Tutorials](#Tutorials)\n- [Publications and Citation](#publications-and-citation)\n- [Contribution](#contribution)\n- [About](#about)\n\n## Installation\n\nNote that SpikingJelly is based on PyTorch. Please make sure that you have installed PyTorch before you install SpikingJelly.\n\n**Version notes**\n\nThe odd version number is the developing version, updated with the GitHub/OpenI repository. The even version number is the stable version and is available at PyPI. \n\nThe default doc is for the latest developing version. If you are using the stable version, do not forget to switch to the doc in the corresponding version.\n\nFrom the version `0.0.0.0.14`, modules including `clock_driven` and `event_driven` are renamed. Please refer to the tutorial [Migrate From Old Versions](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/migrate_from_legacy.html).\n\nIf you use an old version of SpikingJelly, you may encounter some fatal bugs. Refer to [Bugs History with Releases](./bugs.md) for more details.\n\n**Docs for different versions:**\n\n- [zero](https://spikingjelly.readthedocs.io/zh_CN/zero/)\n- [0.0.0.0.4](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.4/#index-en)\n- [0.0.0.0.6](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.6/#index-en)\n- [0.0.0.0.8](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.8/#index-en)\n- [0.0.0.0.10](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.10/#index-en)\n- [0.0.0.0.12](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.12/#index-en)\n- [0.0.0.0.14](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/#index-en)\n- [latest](https://spikingjelly.readthedocs.io/zh_CN/latest/#index-en)\n\n**Install the last stable version from** [**PyPI**](https://pypi.org/project/spikingjelly/):\n\n```bash\npip install spikingjelly\n```\n\n**Install the latest developing version from the source code**:\n\nFrom [GitHub](https://github.com/fangwei123456/spikingjelly):\n```bash\ngit clone https://github.com/fangwei123456/spikingjelly.git\ncd spikingjelly\npython setup.py install\n```\nFrom [OpenI](https://openi.pcl.ac.cn/OpenI/spikingjelly):\n```bash\ngit clone https://openi.pcl.ac.cn/OpenI/spikingjelly.git\ncd spikingjelly\npython setup.py install\n```\n## Build SNN In An Unprecedented Simple Way\n\nSpikingJelly is user-friendly. Building SNN with SpikingJelly is as simple as building ANN in PyTorch:\n\n```python\nnn.Sequential(\n        layer.Flatten(),\n        layer.Linear(28 * 28, 10, bias=False),\n        neuron.LIFNode(tau=tau, surrogate_function=surrogate.ATan())\n        )\n```\n\nThis simple network with a Poisson encoder can achieve 92% accuracy on the MNIST test dataset. Read refer to the tutorial for more details. You can also run this code in a Python terminal for training on classifying MNIST:\n\n```python\npython -m spikingjelly.activation_based.examples.lif_fc_mnist -tau 2.0 -T 100 -device cuda:0 -b 64 -epochs 100 -data-dir \u003cPATH to MNIST\u003e -amp -opt adam -lr 1e-3 -j 8\n```\n\n## Fast And Handy ANN-SNN Conversion\n\nSpikingJelly implements a relatively general ANN-SNN Conversion interface. Users can realize the conversion through PyTorch. What's more, users can customize the conversion mode. \n\n```python\nclass ANN(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.network = nn.Sequential(\n            nn.Conv2d(1, 32, 3, 1),\n            nn.BatchNorm2d(32, eps=1e-3),\n            nn.ReLU(),\n            nn.AvgPool2d(2, 2),\n\n            nn.Conv2d(32, 32, 3, 1),\n            nn.BatchNorm2d(32, eps=1e-3),\n            nn.ReLU(),\n            nn.AvgPool2d(2, 2),\n\n            nn.Conv2d(32, 32, 3, 1),\n            nn.BatchNorm2d(32, eps=1e-3),\n            nn.ReLU(),\n            nn.AvgPool2d(2, 2),\n\n            nn.Flatten(),\n            nn.Linear(32, 10)\n        )\n\n    def forward(self,x):\n        x = self.network(x)\n        return x\n```\n\nThis simple network with analog encoding can achieve 98.44% accuracy after conversion on MNIST test dataset. Read the tutorial for more details. You can also run this code in a Python terminal for training on classifying MNIST using the converted model:\n\n```python\n\u003e\u003e\u003e import spikingjelly.activation_based.ann2snn.examples.cnn_mnist as cnn_mnist\n\u003e\u003e\u003e cnn_mnist.main()\n```\n\n## CUDA-Enhanced Neuron\n\nSpikingJelly provides two backends for multi-step neurons. You can use the user-friendly `torch` backend for easily coding and debugging and use `cupy` backend for faster training speed.\n\nThe following figure compares the execution time of two backends of Multi-Step LIF neurons (`float32`):\n\n\u003cimg src=\"./docs/source/_static/tutorials/activation_based/11_cext_neuron_with_lbl/exe_time_fb.png\" alt=\"exe_time_fb\"  /\u003e\n\n`float16` is also provided by the `cupy` backend and can be used in [automatic mixed precision training](https://pytorch.org/docs/stable/notes/amp_examples.html).\n\nTo use the `cupy` backend, please install [CuPy](https://docs.cupy.dev/en/stable/install.html). Note that the `cupy` backend only supports GPU, while the `torch` backend supports both CPU and GPU.\n\n## Device Supports\n\n-   [x] Nvidia GPU\n-   [x] CPU\n\nAs simple as using PyTorch.\n\n```python\n\u003e\u003e\u003e net = nn.Sequential(layer.Flatten(), layer.Linear(28 * 28, 10, bias=False), neuron.LIFNode(tau=tau))\n\u003e\u003e\u003e net = net.to(device) # Can be CPU or CUDA devices\n```\n\n## Neuromorphic Datasets Supports\nSpikingJelly includes the following neuromorphic datasets:\n\n| Dataset | Source |\n| -------------- | ------------------------------------------------------------ |\n| ASL-DVS        | [Graph-based Object Classification for Neuromorphic Vision Sensing](https://openaccess.thecvf.com/content_ICCV_2019/html/Bi_Graph-Based_Object_Classification_for_Neuromorphic_Vision_Sensing_ICCV_2019_paper.html) |\n| CIFAR10-DVS    | [CIFAR10-DVS: An Event-Stream Dataset for Object Classification](https://internal-journal.frontiersin.org/articles/10.3389/fnins.2017.00309/full) |\n| DVS128 Gesture | [A Low Power, Fully Event-Based Gesture Recognition System](https://openaccess.thecvf.com/content_cvpr_2017/html/Amir_A_Low_Power_CVPR_2017_paper.html) |\n| ES-ImageNet    | [ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks](https://www.frontiersin.org/articles/10.3389/fnins.2021.726582/full) |\n| HARDVS | [HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors](https://arxiv.org/abs/2211.09648) |\n| N-Caltech101   | [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://www.frontiersin.org/articles/10.3389/fnins.2015.00437/full) |\n| N-MNIST        | [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://www.frontiersin.org/articles/10.3389/fnins.2015.00437/full) |\n| Nav Gesture    | [Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities](https://www.frontiersin.org/articles/10.3389/fnins.2020.00275/full) |\n| Spiking Heidelberg Digits (SHD) | [The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks](https://doi.org/10.1109/TNNLS.2020.3044364) |\n| DVS-Lip | [Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading](https://openaccess.thecvf.com/content/CVPR2022/html/Tan_Multi-Grained_Spatio-Temporal_Features_Perceived_Network_for_Event-Based_Lip-Reading_CVPR_2022_paper.html) |\n\nUsers can use both the origin event data and frame data integrated by SpikingJelly:\n\n```python\nimport torch\nfrom torch.utils.data import DataLoader\nfrom spikingjelly.datasets import pad_sequence_collate, padded_sequence_mask\nfrom spikingjelly.datasets.dvs128_gesture import DVS128Gesture\n\n# Set the root directory for the dataset\nroot_dir = 'D:/datasets/DVS128Gesture'\n# Load event dataset\nevent_set = DVS128Gesture(root_dir, train=True, data_type='event')\nevent, label = event_set[0]\n# Print the keys and their corresponding values in the event data\nfor k in event.keys():\n    print(k, event[k])\n\n# t [80048267 80048277 80048278 ... 85092406 85092538 85092700]\n# x [49 55 55 ... 60 85 45]\n# y [82 92 92 ... 96 86 90]\n# p [1 0 0 ... 1 0 0]\n# label 0\n\n# Load a dataset with fixed frame numbers\nfixed_frames_number_set = DVS128Gesture(root_dir, train=True, data_type='frame', frames_number=20, split_by='number')\n# Randomly select two frames and print their shapes\nrand_index = torch.randint(low=0, high=fixed_frames_number_set.__len__(), size=[2])\nfor i in rand_index:\n    frame, label = fixed_frames_number_set[i]\n    print(f'frame[{i}].shape=[T, C, H, W]={frame.shape}')\n\n# frame[308].shape=[T, C, H, W]=(20, 2, 128, 128)\n# frame[453].shape=[T, C, H, W]=(20, 2, 128, 128)\n\n# Load a dataset with a fixed duration and print the shapes of the first 5 samples\nfixed_duration_frame_set = DVS128Gesture(root_dir, data_type='frame', duration=1000000, train=True)\nfor i in range(5):\n    x, y = fixed_duration_frame_set[i]\n    print(f'x[{i}].shape=[T, C, H, W]={x.shape}')\n\n# x[0].shape=[T, C, H, W]=(6, 2, 128, 128)\n# x[1].shape=[T, C, H, W]=(6, 2, 128, 128)\n# x[2].shape=[T, C, H, W]=(5, 2, 128, 128)\n# x[3].shape=[T, C, H, W]=(5, 2, 128, 128)\n# x[4].shape=[T, C, H, W]=(7, 2, 128, 128)\n\n# Create a data loader for the fixed duration frame dataset and print the shapes and sequence lengths\ntrain_data_loader = DataLoader(fixed_duration_frame_set, collate_fn=pad_sequence_collate, batch_size=5)\nfor x, y, x_len in train_data_loader:\n    print(f'x.shape=[N, T, C, H, W]={tuple(x.shape)}')\n    print(f'x_len={x_len}')\n    mask = padded_sequence_mask(x_len)  # mask.shape = [T, N]\n    print(f'mask=\\n{mask.t().int()}')\n    break\n\n# x.shape=[N, T, C, H, W]=(5, 7, 2, 128, 128)\n# x_len=tensor([6, 6, 5, 5, 7])\n# mask=\n# tensor([[1, 1, 1, 1, 1, 1, 0],\n#         [1, 1, 1, 1, 1, 1, 0],\n#         [1, 1, 1, 1, 1, 0, 0],\n#         [1, 1, 1, 1, 1, 0, 0],\n#         [1, 1, 1, 1, 1, 1, 1]], dtype=torch.int32)\n```\nMore datasets will be included in the future.\n\nIf some datasets' download links are not available for some users, the users can download from the OpenI mirror:\n\nhttps://openi.pcl.ac.cn/OpenI/spikingjelly/datasets?type=0\n\nAll datasets saved in the OpenI mirror are allowable by their license or author's agreement.\n\n## Tutorials\n\nSpikingJelly provides elaborate tutorials. Here are some tutorials:\n\n| Figure                                                       | Tutorial                                                     |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| ![basic_concept](./docs/source/_static/tutorials/activation_based/basic_concept/step-by-step.png) | [Basic Conception](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/basic_concept.html) |\n| ![neuron](./docs/source/_static/tutorials/activation_based/neuron/0.png) | [Neuron](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/neuron.html) |\n| ![lif_fc_mnist](./docs/source/_static/tutorials/activation_based/lif_fc_mnist/2d_heatmap.png) | [Single Fully Connected Layer SNN to Classify MNIST](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/lif_fc_mnist.html) |\n| ![conv_fashion_mnist](./docs/source/_static/tutorials/activation_based/conv_fashion_mnist/visualization/2/s_0.png) | [Convolutional SNN to Classify FMNIST](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/conv_fashion_mnist.html) |\n| ![ann2snn](./docs/source/_static/tutorials/activation_based/5_ann2snn/2.png) | [ANN2SNN](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/ann2snn.html) |\n| ![neuromorphic_datasets](./docs/source/_static/tutorials/activation_based/neuromorphic_datasets/dvsg.gif) | [Neuromorphic Datasets Processing](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/neuromorphic_datasets.html) |\n| ![classify_dvsg](./docs/source/_static/tutorials/activation_based/classify_dvsg/network.png) | [Classify DVS Gesture](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/classify_dvsg.html) |\n| ![recurrent_connection_and_stateful_synapse](./docs/source/_static/tutorials/activation_based/recurrent_connection_and_stateful_synapse/ppt/nets.png) | [Recurrent Connection and Stateful Synapse](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/recurrent_connection_and_stateful_synapse.html) |\n| ![stdp_learning](./docs/source/_static/tutorials/activation_based/stdp/mstdp.png) | [STDP Learning](https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.14/activation_based_en/stdp.html) |\n| ![reinforcement_learning](./docs/source/_static/tutorials/activation_based/snn_for_rl/snn_for_rl.png) | [Reinforcement Learning](https://spikingjelly.readthedocs.io/zh-cn/latest/activation_based/ilc_san.html) |\n\nOther tutorials that are not listed here are also available at the document https://spikingjelly.readthedocs.io.\n\n## Publications and Citation\n\nPublications using SpikingJelly are recorded in [Publications](./publications.md). If you use SpikingJelly in your paper, you can also add it to this table by pull request.\n\nIf you use SpikingJelly in your work, please cite it as follows:\n\n```\n@article{\ndoi:10.1126/sciadv.adi1480,\nauthor = {Wei Fang  and Yanqi Chen  and Jianhao Ding  and Zhaofei Yu  and Timothée Masquelier  and Ding Chen  and Liwei Huang  and Huihui Zhou  and Guoqi Li  and Yonghong Tian },\ntitle = {SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence},\njournal = {Science Advances},\nvolume = {9},\nnumber = {40},\npages = {eadi1480},\nyear = {2023},\ndoi = {10.1126/sciadv.adi1480},\nURL = {https://www.science.org/doi/abs/10.1126/sciadv.adi1480},\neprint = {https://www.science.org/doi/pdf/10.1126/sciadv.adi1480},\nabstract = {Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing. Motivation and introduction of the software framework SpikingJelly for spiking deep learning.}}\n```\n\n## Contribution\n\nYou can read the issues and get the problems to be solved and the latest development plans. We welcome all users to join the discussion of development plans, solve issues, and send pull requests.\n\nNot all API documents are written in both English and Chinese. We welcome users to complete translation (from English to Chinese or from Chinese to English).\n\n## About\n\n[Multimedia Learning Group, Institute of Digital Media (NELVT), Peking University](https://pkuml.org/) and [Peng Cheng Laboratory](http://www.szpclab.com/) are the main developers of SpikingJelly.\n\n\u003cimg src=\"./docs/source/_static/logo/pku.png\" alt=\"PKU\" width=\"160\" /\u003e\n\n\u003cimg src=\"./docs/source/_static/logo/pcl.png\" alt=\"PCL\" width=\"160\" /\u003e\n\nThe list of developers can be found [here](https://github.com/fangwei123456/spikingjelly/graphs/contributors).\n\n## All Thanks to Our Contributors\n\n\u003ca href=\"https://github.com/fangwei123456/spikingjelly/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=fangwei123456/spikingjelly\" /\u003e\n\u003c/a\u003e\n\n\u003cp align=\"right\"\u003e\u003ca href=\"#top\"\u003e\u003cimg src=\"https://cdn-icons-png.flaticon.com/512/892/892692.png\" height=\"50px\"\u003e\u003c/a\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffangwei123456%2Fspikingjelly","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffangwei123456%2Fspikingjelly","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffangwei123456%2Fspikingjelly/lists"}