AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 1529

Discover models worth your attention

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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This 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.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This 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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This 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.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This 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

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This 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 ---

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

__NOTE: Like all models, this one can make mistakes. NSFW content can be subjective and contextual, this model is intended to help identify this content, use at your own risk.__ `Marqo/nsfw-image-detection-384` is a lightweight image classification model designed to identify NSFW images. The model is approximately 18–20x smaller than other open-source models and achieves a superior accuracy of 98.56% on our dataset. This model uses 384x384 pixel images for the input with 16x16 pixel patches. This model was trained on a proprietary dataset of 220,000 images. The training set includes 100,000 NSFW examples and 100,000 SFW examples, while the test set contains 10,000 NSFW examples and 10,000 SFW examples. This dataset features a diverse range of content, including: real photos, drawings, Rule 34 material, memes, and AI-generated images. The definition of NSFW can vary and is sometimes contextual, our dataset was constructed to contain challenging examples however this definition may not be 100% aligned with every use case, as such we recommend experimenting and trying different thresholds to determine if this model is suitable for your needs.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

The SwiftFormer model was proposed in SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2. url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw)

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

RitualCNN

methil-group

The ONNX file is `RitualCNN.onnx`. It accepts a grayscale image as a `float32` tensor named `input`, with shape `[batch, 1, 128, 128]`. Before inference, convert the image to grayscale, resize it to 128 by 128 pixels, set pixels above 127 to 1 and all other pixels to 0. The model returns seven raw scores named `output`. The highest score is the predicted class. These are model inputs from the held-out validation set after preprocessing. When a class has no correct validation predictions, its row shows the highest-scoring example. The top softmax score is not a calibrated probability.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches.

Image classification
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗