mask2former-swin-large-ade-semantic
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve facebook/mask2former-swin-large-ade-semanticNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
mask2former-swin-large-ade-semantic is an open-weight image segmentation model from facebook, published on Hugging Face in January 2023. It has 220M parameters and is released under a custom license (see the model card).
HardwareLink to this section
At full FP16 precision the weights need about 1 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
mask2former-swin-large-ade-semantic had 351,552 downloads on Hugging Face in the last month and has 33 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Transformers. Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does mask2former-swin-large-ade-semantic need?
mask2former-swin-large-ade-semantic with 0.22B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is mask2former-swin-large-ade-semantic better than other facebook models?
mask2former-swin-large-ade-semantic has 0.22B parameters with 8,192 context - a strong choice for image-segmentation.
What license is mask2former-swin-large-ade-semantic under?
mask2former-swin-large-ade-semantic is released under the Other license, making it suitable for most commercial and personal projects.
What hardware runs mask2former-swin-large-ade-semantic well?
With 0.22B parameters, mask2former-swin-large-ade-semantic requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for mask2former-swin-large-ade-semantic?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with mask2former-swin-large-ade-semantic?
mask2former-swin-large-ade-semantic competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec