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Download README.md from capicu-ai/cellpose-sam-wquant-w2a16-g64: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/capicu-ai/cellpose-sam-wquant-w2a16-g64/resolve/8672e50089fd3daa1213cee098e4b5dcc0455624/README.md
- Command line
-
hf download hf://capicu-ai/cellpose-sam-wquant-w2a16-g64@8672e50089fd3daa1213cee098e4b5dcc0455624/README.md
-
curl -L -o README.md https://huggingface.co/capicu-ai/cellpose-sam-wquant-w2a16-g64/resolve/8672e50089fd3daa1213cee098e4b5dcc0455624/README.md
1.11 kB
metadata
library_name: cellpose
pipeline_tag: image-segmentation
base_model: mouseland/cellpose-sam
base_model_relation: quantized
license: bsd-3-clause
tags:
- cellpose
- cellpose-sam
- microscopy
- quantization
- image-segmentation
Cellpose-SAM WQuant — W2A16-G64
Ready-to-load W2A16-G64 quantized variant of
Cellpose-SAM cpsam_v2.
Load
import runpy
from huggingface_hub import hf_hub_download
loader = runpy.run_path(
hf_hub_download("capicu-ai/cellpose-sam-wquant-w2a16-g64", "runtime_loader.py")
)
model = loader["load_model"]()
Pass device="cuda" to load_model for CUDA.
Result
- Weight size: 96.3 MiB
- Compression versus FP32: 12.08x
- Evaluated paired samples: 20
- Complete bounded-panel criterion met: No
- Reload verification normalized RMSE: 0.0
results.json and model_registry.json contain the compact machine-readable
record.