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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 300
yolos-base-signature-detection
mdefrance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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查看详情 ↗yolos-tiny-signature-detection
mdefranceYOLOS model finetuned to detect handwritten signatures in document images using [tech4humans/signature-detection](https://huggingface.co/datasets/tech4humans/signature-detection) dataset. Original YOLOS was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
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查看详情 ↗UrbanOmniDetect
mehmetkeremturkcan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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查看详情 ↗UrbanOmniDetect-2
mehmetkeremturkcan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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查看详情 ↗human-fall-detection
melihuzunoglu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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查看详情 ↗ppe-detection
melihuzunoglu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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查看详情 ↗figpanel-yolov12
mermermer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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查看详情 ↗fire-smoke-yolov8
mfranzon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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conditional-detr-resnet-50
MicrosoftConditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Conditional DETR for Fast Training Convergence by Meng et al. and first released in this repository. The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. You can use the raw model for object detection. See the model hub to look for all available Conditional DETR models.
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查看详情 ↗table-transformer-detection
MicrosoftTable Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
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查看详情 ↗Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
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查看详情 ↗Table Transformer (TATR) model trained on PubTables1M and FinTabNet.c. It was introduced in the paper Aligning benchmark datasets for table structure recognition by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗Ultralytics
MidnightRunner3. Place your .pt files: • Segmentation → models/ultralytics/segm/ • Detection (bbox) → models/ultralytics/bbox/ 4. Restart ComfyUI 5. In the UI: Add Node → Model → Ultralytics → choose segm/… or bbox/…. Connect an Image Loader → Ultralytics node → Previewer Since getattr is classified as a dangerous pickle function, any segmentation model that uses it is classified as unsafe.
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查看详情 ↗yolov11-license-plate-detection
morsetechlabThis is a fine-tuned version of YOLOv11 (n, s, m, l, x) specialized for **License Plate Detection**, using a public dataset from Roboflow Universe: License Plate Recognition Dataset (10,125 images) The upstream Roboflow dataset (`license-plate-recognition-rxg4e`) contains **train/test contamination** — the same source images appear in both the training and test splits with only minor manual augmentation applied (see Discussion #2 for concrete examples). As a result: - **The reported metrics below are likely overestimated**, because the test set is not a true held-out evaluation. - Real-world generalization performance is expected to be lower than the numbers in the table. - Treat all evaluation figures with caution and validate the model on your own held-out data before production use.
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查看详情 ↗This repository contains a **YOLOv12x** object detection model fine-tuned to detect individual panels in comic book pages. The model identifies the bounding boxes for each panel, making it a valuable tool for digitizing comics, extracting content, or building datasets for downstream analysis. This model was trained in PyTorch using the powerful `ultralytics` library and demonstrates high performance on a custom-annotated dataset of comic pages. *Visit this space to try out the model right now: `The_Best_Comic_Panel_Detection`.*
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查看详情 ↗pcb-aoi-yolo-rmutt
Moszer777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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查看详情 ↗LibreGemma4e4b-i1-GGUF
mradermacher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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查看详情 ↗Polaris-VGA-0.8B-Post1.0-GGUF
mradermacher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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mradermacher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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查看详情 ↗Polaris-VGA-27B-Post1.0e-i1-GGUF
mradermacher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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查看详情 ↗Polaris-VGA-2B-Post1.0-GGUF
mradermacher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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。