decider-2b-vision
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85003
decider-31b
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查看详情 ↗decider-35b-a3b
Mapika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 ↗decider-4b
MapikaA language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v3 the state may include images. **This repository holds v3 (2026-10-20).** v2.1 stays available under the Hub tag `v2.1`, with its own card; v2 and v1 under `v2` and `v1`. - **Weights:** the text model at the repository root (Qwen3.5-4B-Base architecture, bf16, 8.4 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-4B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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查看详情 ↗decider-4b-GGUF
MapikaGGUF files of Mapika/decider-4b v3, for llama.cpp. decider-4b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-4b card for what the model is, how it was trained, and where it is weak. **This repository holds the v3 files (2026-10-20).** The v2.1 files stay available under the Hub tag `v2.1`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-4b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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查看详情 ↗decider-chat-gemma4-31b
Mapika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 ↗RemeDi-RL
maple-research-lab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 ↗TimeMoE-200M
Maple728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 ↗TimeMoE-50M
Maple728This repository contains the weights of the TimeMoE-50M model of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
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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 ↗Qwen3.5-27B-heretic-v3-i1-GGUF
marafx2007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 ↗Qwen3.8-9B-heretic-uncensored-i1-GGUF
marafx2007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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价格待核验多模态与渠道费用须分别核对
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价格待核验多模态与渠道费用须分别核对
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查看详情 ↗MaralGPT-Mythos-9B-2606-GGUF
MaralGPT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 ↗This model was trained on dataset `Marc-HealthAI/fetal-planes-classification-dataset-main`. Change the lr to 5e-5 from 1e-4 and made effective batch 32 * **Classes:** Fetal abdomen, Fetal brain, Fetal femur, Fetal thorax, Maternal cervix, Other * **Dataset Features:** `['Image_name', 'Patient_num', 'Plane', 'Brain_plane', 'Operator', 'US_Machine', 'Split', 'image']` **Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5** A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients. The dataset details are described in our open-acces paper: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
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查看详情 ↗Crimson-Flame-6-resNet18-v2-african-planes
Marc-HealthAIThis model is a fine-tuned ResNet-18 trained to classify fetal ultrasound standard planes on the African planes dataset. This resource is a dataset of routinely acquired maternal-fetal screening ultrasound images collected in five centers of five countries in Africa (Malawi, Egypt, Uganda, Ghana and Algeria) that is associated to the journal article Sendra-Bacells et al. "Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries", Scientific Reports. The images correspond to the four most common fetal planes: abdomen, brain, femur and thorax. A CSV file is provided where image filenames are associated to plane types and patient number as well as the partitioning in training and testing splits as used in the associated publication.
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查看详情 ↗Crimson-Moon-6-DenseNet161-v2
Marc-HealthAIThis model was trained on dataset `Marc-HealthAI/fetal-planes-classification-dataset-main`. Retrain with updated lr=1e-5 and effective batch size 32 * **Classes:** Fetal abdomen, Fetal brain, Fetal femur, Fetal thorax, Maternal cervix, Other * **Dataset Features:** `['Image_name', 'Patient_num', 'Plane', 'Brain_plane', 'Operator', 'US_Machine', 'Split', 'image']` **Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5** A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients. The dataset details are described in our open-acces paper: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Stellar-Class-5L-6-E1-NoFinalPool
Marc-HealthAIThis model is a custom 5-block convolutional network, trained from scratch (no ImageNet pretraining) on single-channel 128x128 fetal ultrasound images, to classify fetal ultrasound standard planes. No explicit description provided in dataset metadata. Changes Made Removed the pooling on the final layer. Lowered lr = 1e-5 effective batch size 32 --- **Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5** A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients.
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查看详情 ↗Stellar-Class-5L-6-E2-Residual
Marc-HealthAIThis model is a custom 5-block convolutional network, trained from scratch (no ImageNet pretraining) on single-channel 128x128 fetal ultrasound images, to classify fetal ultrasound standard planes. **Burgos-Artizzu, X.P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10, 10200 (2020). https://doi.org/10.1038/s41598-020-67076-5** A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician (B.V-A.). Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients. The dataset details are described in our open-acces paper: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
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价格待核验多模态与渠道费用须分别核对
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。