Metadata-Version: 2.4
Name: zeromodels
Version: 1.2.9
Summary: ZeroModels: Open-source Keras 3 collection of pretrained models
Author-email: Gitesh Chawda <gitesh.ch.0912@gmail.com>
License: Apache License 2.0
Project-URL: homepage, https://imvision12.github.io/ZeroModels/
Project-URL: documentation, https://imvision12.github.io/ZeroModels/
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Keywords: machine-learning,jax,computer-vision,neural-networks,tensorflow,torch,deep-learning,keras,imagenet,pretrained-weights,convolutional-neural-networks,transfer-learning,python-ml,data-science,ai-research,vision-transformer,image-classification,model-training,pytorch
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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License-File: LICENSE
Requires-Dist: keras>=3.15.0
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Provides-Extra: test
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Requires-Dist: torch>=2.5.0; extra == "conversion"
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<p align="center">
  <img src="https://github.com/user-attachments/assets/34764a5a-5959-4c76-b5b8-3fc1fd3b52c5" width="100">
</p>
<h1 align="center">ZeroModels</h1>

<div align="center">

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Keras](https://img.shields.io/badge/keras-v3.5.0+-success.svg)](https://github.com/keras-team/keras)
![Python](https://img.shields.io/badge/python-v3.10.0+-success.svg)

</div>

## 📖 Introduction

ZeroModels is a collection of pretrained models built entirely in Keras 3. It spans a broad range of tasks, including image classification, object detection, segmentation, monocular depth estimation, feature extraction, vision-language modeling (VLMs), speech recognition, speech-aware language modeling, text encoding and masked language modeling, large language models (LLMs), text-to-text encoder-decoder modeling, multimodal vision-language generation, and more.

## ⚡ Installation

From PyPI (recommended)

```shell
pip install -U zeromodels
```

From Source

```shell
pip install -U git+https://github.com/IMvision12/ZeroModels
```

## 📑 Documentation

**[ZeroModels Documentation](https://imvision12.github.io/ZeroModels/)**

Detailed guides are available for all supported tasks, with architecture notes, usage examples, pretrained weights, and real model outputs.

Classification backbones share a single [documentation page](https://imvision12.github.io/ZeroModels/classification_backbones/), while other model families have dedicated pages.

Documentation sources are also available in [`docs/`](docs/).


## 📑 Models

### 📝 Text Models

- Text Encoders (text → embeddings, masked LM, classification)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | BERT | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) | `transformers` |
    | ModernBERT | [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder](https://arxiv.org/abs/2412.13663) | `transformers` |
    | ELECTRA | [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/abs/2003.10555) | `transformers` |
    | RoBERTa | [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) | `transformers` |
    | XLM-RoBERTa | [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) | `transformers` |
    | DeBERTa | [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) | `transformers` |
    | DeBERTa-v2 | [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) | `transformers` |
    | DeBERTa-v3 | [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://arxiv.org/abs/2111.09543) | `transformers` |
    | T5 | [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) | `transformers` |

- Text LLMs (text → text)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | Qwen2 | [Qwen2 Technical Report](https://arxiv.org/abs/2407.10671) | `transformers` |
    | Qwen2-MoE | [Qwen2 Technical Report](https://arxiv.org/abs/2407.10671) | `transformers` |
    | Qwen3 | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | Qwen3-MoE | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | Qwen3.5 | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | Qwen3-Next | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | GPT-OSS | [gpt-oss-120b & gpt-oss-20b Model Card](https://arxiv.org/abs/2508.10925) | `transformers` |
    | GPT | [Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) | `transformers` |
    | GPT-2 | [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | `transformers` |
    | Llama 2 | [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/abs/2307.09288) | `transformers` (gated) |
    | Llama 3 | [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) | `transformers` (gated) |
    | Llama 4 | [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) | `transformers` (gated) |
    | Mistral | [Mistral 7B](https://arxiv.org/abs/2310.06825) | `transformers` (gated) |
    | Mixtral | [Mixtral of Experts](https://arxiv.org/abs/2401.04088) | `transformers` (gated) |
    | DeepSeek-V2 | [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) | `transformers` |
    | DeepSeek-V3 | [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) | `transformers` |
    | DeepSeek-V4 | [DeepSeek-V4 Technical Report](https://arxiv.org/abs/2606.19348) | `transformers` |
    | Gemma | [Gemma: Open Models Based on Gemini Research and Technology](https://arxiv.org/abs/2403.08295) | `transformers` (gated) |
    | Gemma 2 | [Gemma 2: Improving Open Language Models at a Practical Size](https://arxiv.org/abs/2408.00118) | `transformers` (gated) |
    | MiniMax-Text-01 | [MiniMax-01: Scaling Foundation Models with Lightning Attention](https://arxiv.org/abs/2501.08313) | `transformers` |
    | MiniMax-M2 | [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) | `transformers` |
    | GLM-4 (GLM-4-9B) | [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4](https://arxiv.org/abs/2406.12793) | `transformers` |
    | GLM-4-0414 | [THUDM/GLM-4-9B-0414](https://huggingface.co/THUDM/GLM-4-9B-0414) | `transformers` |
    | GLM-4.5 / GLM-4.6 (MoE) | [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://arxiv.org/abs/2508.06471) | `transformers` |
    | GLM-5 / GLM-5.1 / GLM-5.2 (MoE) | [GLM-5 Technical Report](https://arxiv.org/abs/2602.15763) | `transformers` |

<br>

### 👁️ Vision Models

- Backbones

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | BEiT | [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) | `transformers` |
    | CaiT | [Going deeper with Image Transformers](https://arxiv.org/abs/2103.17239) | `timm` |
    | ConvMixer | [Patches Are All You Need?](https://arxiv.org/abs/2201.09792) | `timm` |
    | ConvNeXt | [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) | `timm` |
    | ConvNeXt V2 | [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) | `timm` |
    | DeiT | [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) | `timm` |
    | DenseNet | [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) | `timm` |
    | EfficientFormer | [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191) | `timm` |
    | EfficientNet | [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) | `timm` |
    | EfficientNet-Lite | [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) | `timm` |
    | EfficientNetV2 | [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298) | `timm` |
    | FlexiViT | [FlexiViT: One Model for All Patch Sizes](https://arxiv.org/abs/2212.08013) | `timm` |
    | InceptionNeXt | [InceptionNeXt: When Inception Meets ConvNeXt](https://arxiv.org/abs/2303.16900) | `timm` |
    | Inception-ResNet-v2 | [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) | `timm` |
    | Inception-v3 | [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567) | `timm` |
    | Inception-v4 | [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) | `timm` |
    | LeViT | [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) | `transformers` |
    | MaxViT | [MaxViT: Multi-Axis Vision Transformer](https://arxiv.org/abs/2204.01697) | `timm` |
    | MiT | [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) | `transformers` |
    | MLP-Mixer | [MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/abs/2105.01601) | `timm` |
    | MobileNetV2 | [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) | `timm` |
    | MobileNetV3 | [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) | `keras` |
    | MobileNetV4 | [MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518) | `keras` |
    | MobileViT | [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) | `transformers` |
    | MobileViTV2 | [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) | `transformers` |
    | NextViT | [Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios](https://arxiv.org/abs/2207.05501) | `timm` |
    | PiT | [Rethinking Spatial Dimensions of Vision Transformers](https://arxiv.org/abs/2103.16302) | `timm` |
    | PoolFormer | [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) | `timm` |
    | PVT | [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) | `transformers` |
    | PVTv2 | [PVTv2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) | `transformers` |
    | RegNet | [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) | `transformers` |
    | Res2Net | [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169) | `timm` |
    | ResMLP | [ResMLP: Feedforward networks for image classification with data-efficient training](https://arxiv.org/abs/2105.03404) | `timm` |
    | ResNet | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) | `timm` |
    | ResNetV2 | [Identity Mappings in Deep Residual Networks](https://arxiv.org/abs/1603.05027) | `timm` |
    | ResNeXt | [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431) | `timm` |
    | SENet | [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507) | `timm` |
    | Swin Transformer | [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) | `timm` |
    | Swin Transformer V2 | [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) | `timm` |
    | VGG | [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) | `timm` |
    | ViT | [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) | `timm` |
    | Xception | [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357) | `timm` |

<br>

- Object Detection

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | D-FINE | [D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) | `transformers` |
    | DETR | [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) | `transformers` |
    | Table Transformer | [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) | `transformers` |
    | EfficientDet | [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) | `automl` |
    | RT-DETR | [DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069) | `transformers` |
    | RT-DETRv2 | [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformers](https://arxiv.org/abs/2407.17140) | `transformers` |
    | RF-DETR | [RF-DETR: Neural Architecture Search for Real-Time Detection Transformers](https://arxiv.org/abs/2511.09554) | `transformers` |
    | OWL-ViT | [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) | `transformers` |
    | OWLv2 | [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) | `transformers` |
    | Grounding DINO | [Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) | `transformers` |

<br>

- Segmentation

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | BEiT | [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) | `transformers` |
    | DeepLabV3 | [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587) | `torchvision` |
    | EoMT | [Your ViT is Secretly an Image Segmentation Model](https://arxiv.org/abs/2503.19108) | `transformers` |
    | MaskFormer | [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) | `transformers` |
    | Mask2Former | [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) | `transformers` |
    | MobileViT | [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) | `transformers` |
    | MobileViTV2 | [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) | `transformers` |
    | OneFormer | [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) | `transformers` |
    | RF-DETR | [RF-DETR: Neural Architecture Search for Real-Time Detection Transformers](https://arxiv.org/abs/2511.09554) | `transformers` |
    | SAM | [Segment Anything](https://arxiv.org/abs/2304.02643) | `transformers` |
    | SAM2 | [SAM 2: Segment Anything in Images and Videos](https://arxiv.org/abs/2408.00714) | `transformers` |
    | SAM3 | [SAM 3: Segment Anything with Concepts](https://arxiv.org/abs/2511.16719) | `transformers` (gated) |
    | SegFormer | [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) | `transformers` |

<br>

- Feature Extraction

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | DINO | [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) | `torch.hub` |
    | DINOv2 | [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) | `transformers` |
    | DINOv3 | [DINOv3: Self-Supervised Visual Representation Learning at Scale](https://arxiv.org/abs/2508.10104) | `transformers` (gated) |

<br>

- Depth Estimation

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | Depth Anything V1 | [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) | `transformers` |
    | Depth Anything V2 | [Depth Anything V2](https://arxiv.org/abs/2406.09414) | `transformers` |
    | TIPSv2-DPT | [TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment](https://huggingface.co/papers/2604.12012) | `transformers` |

<br>

### 🖼️ Multimodal Models

- Vision-Language Encoders

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | CLIP | [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) | `transformers` |
    | MetaCLIP 2 | [MetaCLIP 2: A Worldwide Scaling Recipe](https://arxiv.org/abs/2507.22062) | `transformers` |
    | SigLIP | [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) | `transformers` |
    | SigLIP2 | [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://arxiv.org/abs/2502.14786) | `transformers` |
    | TIPSv2 | [TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment](https://huggingface.co/papers/2604.12012) | `transformers` |

<br>

- Multimodal LLMs (image + text → text)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | Qwen2-VL | [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://arxiv.org/abs/2409.12191) | `transformers` |
    | Qwen2.5-VL | [Qwen2.5-VL Technical Report](https://arxiv.org/abs/2502.13923) | `transformers` |
    | Qwen3-VL | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | Qwen3-VL-MoE | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | Qwen3.5-MoE | [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388) | `transformers` |
    | InternVL3 | [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://arxiv.org/abs/2504.10479) | `transformers` |
    | Gemma 3 | [Gemma 3 Technical Report](https://arxiv.org/abs/2503.19786) | `transformers` (gated) |
    | Gemma 3n | [google/gemma-3n-E4B](https://huggingface.co/google/gemma-3n-E4B) | `transformers` (gated) |
    | Gemma 4 | [Gemma 4 Technical Report](https://arxiv.org/abs/2607.02770) | `transformers` |
    | Gemma 4 Unified | [Gemma 4 Technical Report](https://arxiv.org/abs/2607.02770) | `transformers` |
    | Mistral 3 | [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) | `transformers` |
    | DeepSeek-VL | [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://arxiv.org/abs/2403.05525) | `transformers` |
    | Janus-Pro | [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://arxiv.org/abs/2501.17811) | `transformers` |
    | MiniMax-M3-VL | [MiniMax-M3 Technical Report](https://arxiv.org/abs/2606.13392) | `transformers` |
    | GLM-4V (GLM-4.1V) | [GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://arxiv.org/abs/2507.01006) | `transformers` |
    | GLM-4.5V (MoE) | [GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://arxiv.org/abs/2507.01006) | `transformers` |
    | Kimi K2.5 / K2.6 / K2.7-Code | [Kimi K2.5 Technical Report](https://arxiv.org/abs/2602.02276) | `transformers` |

<br>

- Vision-Language Grounding (object detection, OCR, pointing, referring)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | LocateAnything | [LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding](https://huggingface.co/nvidia/LocateAnything-3B) | `transformers` |

<br>

### 🔊 Audio Models

- Speech (speech → text)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | Whisper | [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) | `transformers` |
    | Speech2Text | [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) | `transformers` |
    | Moonshine | [Moonshine: Speech Recognition for Live Transcription and Voice Commands](https://arxiv.org/abs/2410.15608) | `transformers` |
    | Granite Speech 5 | [Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities](https://arxiv.org/abs/2505.08699) | `transformers` |

<br>

- Speech LLMs (audio + text → text)

    | 🏷️ Model Name | 📜 Reference Paper | 📦 Source of Weights |
    |---------------|-------------------|---------------------|
    | Granite Speech | [Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities](https://arxiv.org/abs/2505.08699) | `transformers` |
    | Granite Speech Plus | [Granite Speech Plus Technical Report](https://arxiv.org/abs/2604.11269) | `transformers` |


## 📜 License

This project leverages [timm](https://github.com/huggingface/pytorch-image-models#licenses) and [transformers](https://github.com/huggingface/transformers#license) for converting pretrained weights from PyTorch to Keras. For licensing details, please refer to the respective repositories.

- 🔖 **zeromodels Code**: This repository is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).


## 🌟 Credits

- The [Keras](https://github.com/keras-team/keras) team for their powerful and user-friendly deep learning framework
- The [Transformers](https://github.com/huggingface/transformers) library for its robust tools for loading and adapting pretrained models
- The [pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models) project for pioneering many computer vision model implementations
- All contributors to the original papers and architectures implemented in this library

## Citing

### BibTeX

```bash
@misc{gc2025zeromodels,
  author = {Gitesh Chawda},
  title = {ZeroModels},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/IMvision12/ZeroModels}}
```
