RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of categories and image types. This model has been trained on a carefully selected dataset, which includes: general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. The accuracy, efficiency, and versatility currently rival leading source-available models. It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount. Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use. To purchase a…
Open-weight model · Image segmentation
maskformer-swin-tiny-coco
by AI at Meta facebook/maskformer-swin-tiny-coco
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Parameters42M
Context—
Weights334.7 MB
Licenseother
AccessOpen weights
Monthly Downloads12.7k
Runs On
What it takes to serve maskformer-swin-tiny-coco (42M 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 | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 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 Sep 18, 2026.
Model Card
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. MaskFormer 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. You can use this particular checkpoint for semantic segmentation. See the model hub to look for other…
Excerpt from the card by AI at Meta, licensed other.
Configuration
- Architecture
- MaskFormerForInstanceSegmentation
- Layers
- 6
- Attention heads
- 8
- Stored precision
- float32
- Model type
- maskformer
Identity and Version
- Repository
- facebook/maskformer-swin-tiny-coco
- Publisher
- AI at Meta
- Task
- Image segmentation
- Modality
- Image
- Library
- transformers
- Parameters
- 42M parameters
- Languages
- Not stated by the source
- Revision
- 347b42f4c1c918246faf1e295fe350df3e5d718f
- First published
- 2022-03-02
- Last updated
- 2023-09-07
Files and Weights
6 files, 334.8 MB in total. The weights are 2 files totalling 334.7 MB in bin, safetensors.
Weights2 files · 334.7 MB
Configuration2 files · 11.2 KB
Documentation1 file · 2.8 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 167.3 MB | 3167fd842c11 |
| pytorch_model.bin | Weights | 167.4 MB | 0bb929483bce |
| config.json | Configuration | 10.8 KB | — |
| preprocessor_config.json | Configuration | 380 B | — |
| README.md | Documentation | 2.8 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 334.7 MB
Download from AI at Meta
Released by AI at Meta through its official repository on Hugging Face.
Built From
- Described by arXiv:2107.06278
- Trained on (disclosed) coco
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 334.7 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About maskformer-swin-tiny-coco
How much GPU memory does maskformer-swin-tiny-coco need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (42M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run maskformer-swin-tiny-coco on?
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.
What license is maskformer-swin-tiny-coco released under?
other, as its publisher declares it. Read the license text before commercial use.
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