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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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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗Stellar-Class-5L-8-E1-NoFinalPool-customCNN
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 ---
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詳細を見る ↗Stellar-Class-5L-8-E2-Residual-customCNN
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.
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