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multiberts-seed_5
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View details ↗multiberts-seed_6
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View details ↗multiberts-seed_7
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View details ↗multiberts-seed_8
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View details ↗multiberts-seed_9
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View details ↗muril-base-cased
GoogleMuRIL: Multilingual Representations for Indian Languages === MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabling masked word predictions) in this repository. We have also released the encoder on TFHub with an additional pre-processing module, that processes raw text into the expected input format for the encoder. You can find more details on MuRIL in this paper. This model uses a BERT base architecture [1] pretrained from scratch using the Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6] Indian languages. We use a training paradigm similar to multilingual bert, with a few modifications as listed:
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View details ↗muril-large-cased
GoogleMultilingual Representations for Indian Languages : A BERT Large (24L) model pre-trained on 17 Indian languages, and their transliterated counterparts. This model uses a BERT large architecture [1] pretrained from scratch using the Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6] Indian languages. We use a training paradigm similar to multilingual bert, with a few modifications as listed:
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗nano-banana
Google推荐使用，Google 最先进的图像生成和编辑模型 Nano-banana，支持文生图、图生图、多图生图 我们基于 gemini-2.5-flash-image-preview 专门画图优化的api模型，支持 dalle\edits 格式、返回url，官方返回空图失败不扣费，优化了支持设置图片比例
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View details ↗Gemini Nano Banana 2.1
GoogleGemini Nano Banana 2.1 API（APIMart）：支持文生图与参考图编辑，官方与 Ext 双线路可选，输出 1K、2K、4K 三档分辨率。开通会员，API 调用立省20%起。
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View details ↗nano-banana-hd
GoogleNano-banana 高清版，生成 2K 高清大图
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View details ↗nano-banana-pro
Google推荐使用，Google 最先进的图像生成和编辑模型 Nano-banana Pro，支持文生图、图生图、多图生图 我们基于 gemini-3-pro-image-preview 专门画图优化的api模型，支持 dalle\edits 格式、返回url，官方返回空图失败不扣费。 可以通过参数 image_size 控制返回 1K,2K,4K (默认1K)
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View details ↗nano-banana-pro-2k
Google推荐使用，Google 最先进的图像生成和编辑模型 Nano-banana Pro，支持文生图、图生图、多图生图 我们基于 gemini-3-pro-image-preview 专门画图优化的api模型，支持 dalle\edits 格式、返回url，官方返回空图失败不扣费。 可以通过参数 image_size 控制返回 1K,2K,4K (默认1K)
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View details ↗nano-banana-pro-4k
Google推荐使用，Google 最先进的图像生成和编辑模型 Nano-banana Pro，支持文生图、图生图、多图生图 我们基于 gemini-3-pro-image-preview 专门画图优化的api模型，支持 dalle\edits 格式、返回url，官方返回空图失败不扣费。 可以通过参数 image_size 控制返回 1K,2K,4K (默认1K)
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View details ↗ncsnpp-bedroom-256
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View details ↗ncsnpp-celebahq-256
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View details ↗ncsnpp-church-256
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View details ↗ncsnpp-ffhq-1024
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View details ↗ncsnpp-ffhq-256
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View details ↗omni_flash-10s
Google谷歌Omni 生成视频接口（Gemini Omni）是谷歌发布的新一代全模态通用AI模型，主打对话式视频生成与精准编辑能力。
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View details ↗Gemini Omni 1.1 Flash Ext API（APIMart）：文生视频时可在请求中自定义时长、分辨率和尺寸，并支持可选图像输入。开通会员，API 调用立省20%起。
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View details ↗owlv2-base-patch16
GoogleThe OWLv2 model (short for Open-World Localization) was proposed in Scaling Open-Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. The model uses a CLIP backbone with a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective.
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View details ↗The OWLv2 model (short for Open-World Localization) was proposed in Scaling Open-Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. The model uses a CLIP backbone with a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective.
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