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

resnet10

helper2424

It's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. This model was converted using an automated JAX to PyTorch conversion pipeline, ensuring: - Weight preservation - Architecture matching - Numerical stability

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

Names a specimen crop at **subfamily, tribe, genus and species** for **400 bark and ambrosia beetle species** (115 genera, 26 tribes, 2 subfamilies), each level with a calibrated probability and a "known / unsure" decision, and reports the deepest level it is confident about: a species, "Genus sp. (species undetermined)", "Tribe (genus undetermined)", "Subfamily (tribe undetermined)" or "unrecognised". It is tuned to **avoid wrong names**: by default it stops at a coarser level rather than guess. The full species list is at the end of this card and in `species.csv`. Files: `model.safetensors` (weights), `deploy.safetensors` (kNN reference bank, validation score distributions), `config.json` (backbone, taxonomy, temperatures, thresholds, test report), `species.csv` (the 400 species), `calibration.md` (how the unknown check and thresholds were chosen). Classification of the benchmark's own species (top-1, no abstention): `iid_test` 650 scored specimens: subfamily 1.000, tribe 0.983, genus 0.922, species 0.838; all 5,017 `iid_test` specimens: genus 0.973, species 0.918; `inat_test` field photos: species 0.784.

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

Imager-1

Imagers

**Imager-1** is an AI model designed to detect AI-generated images with high precision; it can detect DALL-E 2. We will soon launch **imager-1-small** and **imager-2**, which will be able to detect DALL-E 3 and other text-to-image models. This repository contains the **base model** of Imager-1, released for **developers and researchers** who want direct access to the architecture, weights, and training pipeline. Imager always publishes a **separately verified and production-ready version** of each model, tested against real-world generators before release. This base model is provided so the community can build on top of it, fine-tune it, or integrate it into their own systems.

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

skintelligent-acne

imfarzanansari

This model card provides documentation for the Acne Severity Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess acne severity levels, ranging from clear skin to very severe acne. This Acne Severity Detection model checkpoint is licensed under the MIT License. Please review and adhere to the license when using or modifying the code.

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

This model card provides documentation for the Wrinkles Detection model checkpoint used in the Hugging Face pipeline. The model is designed to assess wrinkle / non-wrinkle. The checkpoint includes the following files: - `config.json`: Model configuration settings. - `model.safetensors`: Serialized model parameters and architecture. - `preprocessor_config.json`: Configuration file for the preprocessor. - `training_args.bin`: Binary file storing training arguments.

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

A lightweight binary classifier that tells whether a **document region crop** is legible enough to be sent to an OCR model. It labels each crop `good` or `bad`. It is meant to sit between a layout detector and a (vision-language) OCR model. Crops are produced by a detector such as `Inceptive/PP-DocLayoutV3-ONNX`, and this model flags the ones that are too blurry, pixelated or compression-damaged. The pipeline can then skip those regions instead of running expensive inference on them, since unreadable regions are a common source of hallucinated OCR output. The previous release, `Inceptive/document-gate-keeper-v0`, stays available unchanged. **v0.5 expects different preprocessing**, so do not reuse v0's code (see Note for v0 users).

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 version of google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1924 - Accuracy: 0.9445 This model is a fine-tuned version of a pre-trained Vision Transformer (`google/vit-base-patch16-224`) for image classification on the Oxford-IIIT Pet Dataset. It uses transfer learning to adapt a generic vision model to identify 37 different cat and dog breeds. The model head is adjusted to output the number of classes in the dataset, and it is trained end-to-end using standard classification loss. **Intended Uses:** - Educational demos on transfer learning and fine-tuning vision models. - Pet breed classification in structured datasets similar to Oxford Pets. - Comparative analysis with zero-shot models like CLIP.

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

genderage2

ivensamdh

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。

Basic profile indexed · More details to follow

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

Scores an illustration by **one specific person's** taste — not a universal quality model, so it won't match anyone else's preferences. Output is a single number in `[0, 1]`; higher means more to this person's liking. **Only the head ships here (~7 MB), not an image model.** It runs on top of the frozen `google/siglip2-so400m-patch14-384` backbone, which `silva[backbone]` installs and loads for you. Already have `google/siglip2-so400m-patch14-384` embeddings? Skip the backbone and score them directly:

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 ↗