dit-large
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dit-large-finetuned-rvlcdip
MicrosoftDocument Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP, a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.
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View details ↗dml-ai-hub-models
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View details ↗dolly-v2-7b-olive-optimized
Microsoft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 ↗elem2design
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View details ↗falcon-7B-onnx
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View details ↗Fara-7B
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View details ↗Fara-7B-onnx
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View details ↗Fara1.5-27B
Microsoft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 ↗Fara1.5-4B
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View details ↗Fara1.5-9B
Microsoft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 ↗FlexCAD
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View details ↗Florence-2-base
MicrosoftThis Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + Florence-2 technical report. + Jupyter Notebook for inference and visualization of Florence-2-large model
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View details ↗Florence-2-base-ft
MicrosoftThis Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + Florence-2 technical report. + Jupyter Notebook for inference and visualization of Florence-2-large model
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View details ↗Florence-2-large
Microsoft**This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.
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View details ↗Florence-2-large-ft
MicrosoftThis Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + Florence-2 technical report. + Jupyter Notebook for inference and visualization of Florence-2-large model
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View details ↗focalnet-base
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View details ↗focalnet-base-lrf
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View details ↗focalnet-large-fl4-dino-o365
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View details ↗focalnet-large-fl4-dino-o365-cocoft
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View details ↗focalnet-large-lrf-fl3
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View details ↗focalnet-large-lrf-fl4
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View details ↗focalnet-small
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View details ↗focalnet-small-lrf
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