internimage_g_jointto22k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 1529
internimage_h_22kto1k_640
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_h_jointto22k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_l_22k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_l_22kto1k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_s_1k_224
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_t_1k_224
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_xl_22k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗internimage_xl_22kto1k_384
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b0
OpenGVLabThe Pyramid Vision Transformer v2 (PVTv2) is a powerful, lightweight hierarchical transformer backbone for vision tasks. PVTv2 infuses convolution operations into its transformer layers to infuse properties of CNNs that enable them to learn image data efficiently. This mix transformer architecture requires no added positional embeddings, and produces multi-scale feature maps which are known to be beneficial for dense and fine-grained prediction tasks. Vision models using PVTv2 for a backbone: 1. Segformer for Semantic Segmentation. 2. GLPN for Monocular Depth. 3. Deformable DETR for 2D Object Detection. 4. Panoptic Segformer for Panoptic Segmentation.
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b1
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b2
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b2_linear
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b3
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b4
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pvt_v2_b5
OpenGVLab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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗mobilenet_v1_0.75_192
optimum-intel-internal-testingMobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository. Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team. > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗tiny-random-vit
optimum-intel-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗vit-with-attentions
optimum-intel-internal-testing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。
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗vit-with-hidden_states
optimum-intel-internal-testing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。
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗sdxl-detector
OrganikaThis model was created by fine-tuning the umm-maybe AI art detector on a dataset of Wikimedia-SDXL image pairs, where the SDXL image is generated using a prompt based upon a BLIP-generated caption describing the Wikimedia image. This model demonstrates greatly improved performance over the umm-maybe detector on images generated by more recent diffusion models as well as non-artistic imagery (given the broader range of subjects depicted in the random sample drawn from Wikimedia). However, its performance may be lower for images generated using models other than SDXL. In particular, this model underperforms the original detector for images generated using older models (such as VQGAN+CLIP).
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗image-safety-classifier-l
OwenElliott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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗image-safety-classifier-xs
OwenElliott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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗SafeScan
Parveshiiii**SafeScan** is a multi-class image classifier designed to detect and categorize NSFW (Not Safe For Work) content in images. Built on top of the `google/vit-base-patch16-224` vision transformer backbone, SafeScan classifies visual media across five distinct categories to aid in automated content moderation and platform safety. The model maps visual inputs to the following labels: * **0: safe** – General, non-explicit content * **1: drawing** – Non-explicit artistic illustrations and drawings * **2: sexy** – Suggestive, provocative, or borderline content * **3: hentai** – Explicit anime/manga or illustrated adult content * **4: porn** – Explicit adult photographic media 1. **Multi-Class Granularity:** Differentiates between both real/photographic media and illustrated content across safe, suggestive, and explicit tiers. 2. **Social Media Compression Robustness:** Trained with almost all common social media compression artifacts, ensuring performance stays consistent when analyzing images uploaded across different platforms and formats. 3. **Vision Transformer Backbone:** Leverages `google/vit-base-patch16-224` for high-capacity visual representation learning. 4. **Fast and Scalable:** Standard patch-based transformer architecture suitable for batch processing and automated moderation pipelines.
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
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
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.