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Open-weight model · Image segmentation
by CAMPUS INSTITUTE DATA SCIENCE (CIDAS) CIDAS/clipseg-rd64
clipseg-rd64 is an open-weight model for image segmentation from CAMPUS INSTITUTE DATA SCIENCE (CIDAS), released under Apache License 2.0. It has 151M parameters and a 77-token context. At 16-bit it needs about 0.4 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 14.3k downloads a month.
CLIPSeg model with reduce dimension 64. It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository. This model is intended for zero-shot and one-shot image segmentation.
What it takes to serve clipseg-rd64 (151M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.3 GB | 0.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 13, 2026.
clipseg-rd64 on every accelerator the SAVRN Index prices, at every precision
By CAMPUS INSTITUTE DATA SCIENCE (CIDAS), published under apache-2.0, revision 1c75af8363f2.
CLIPSeg model with reduce dimension 64. It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository. This model is intended for zero-shot and one-shot image segmentation. Refer to the documentation.
CLIPSeg model with reduce dimension 64. It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository.
This model is intended for zero-shot and one-shot image segmentation.
Refer to the documentation.
10 files, 1.2 GB in total. The weights are 2 files totalling 1.2 GB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 602.8 MB | 2e166d09082e |
| pytorch_model.bin | Weights | 602.9 MB | 63e3e34334d7 |
| config.json | Configuration | 4.7 KB | — |
| preprocessor_config.json | Configuration | 379 B | — |
| special_tokens_map.json | Configuration | 472 B | — |
| README.md | Documentation | 552 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| merges.txt | Tokenizer | 524.6 KB | — |
| tokenizer_config.json | Tokenizer | 974 B | — |
| vocab.json | Tokenizer | 1.1 MB | — |
Released by CAMPUS INSTITUTE DATA SCIENCE (CIDAS) through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 1.2 GB |
| 16-bit | 0.3 GB |
| 8-bit | 0.2 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0.4 GB at 16-bit and 0.1 GB at 4-bit: the weights (151M parameters) plus a working margin. A long context needs more.
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Yes. clipseg-rd64 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
77 tokens, from the maximum position embeddings in its published configuration.
CLIPSeg model with reduce dimension 64, refined (using a more complex convolution). It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository. This model is intended for zero-shot and one-shot image segmentation. Refer to the documentation.
Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i)…
Mask2Former model trained on COCO panoptic segmentation (base-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i)…
BEN2 (Background Erase Network) introduces a novel approach to foreground segmentation through its innovative Confidence Guided Matting (CGM) pipeline. The architecture employs a refiner network that targets and processes pixels where the base model exhibits lower confidence levels, resulting in more precise and reliable matting results. This model is built on BEN: BEN2 was trained on the DIS5k and our 22K proprietary segmentation dataset. Our enhanced model delivers superior performance in hair matting, 4K processing, object segmentation, and edge refinement. Our Base model is open source. To try the full model through our free web demo or integrate BEN2 into your project with our API…
Research archive, currently publicly visible on the Hub, of a five-epoch DINOv3 ViT-B/16 multi-task pilot for visible fire/smoke presence, segmentation, point localization and explicit visual abstention. This repository preserves a completed, reloadable experiment. It is not a production model and not independently benchmarked. The retained release records describe a private pilot policy; current public visibility does not resolve that restriction or establish redistribution permission. The word “pilot” is part of the model identity and should not be removed in downstream documentation. The metrics below come from the campaign validation split. They are not an independent benchmark and must…
MicroStructFormer is a high-capacity Mask2Former model built on a Swin-Large backbone, designed for the semantic segmentation of ultrastructures in Electron Microscopy (EM) images. This model has been trained to accurately segment the following critical ultrastructural elements: Note: While the model architecture theoretically supports axon and myelin segmentation, this specific release is optimized and calibrated for the classes listed below due to dataset annotations. You can use this model easily via the transformers library. This Hugging Face model repository is associated with the following bioRxiv preprint: These model weights and associated architectures are provided for…
