Segment Anything 2.1 (SAM 2.1), image (fastforks)
Segment Anything 2.1 (Ravi et al. 2024), image mode: promptable segmentation with a hierarchical Hiera image encoder, an FPN neck and the SAM prompt encoder / mask decoder with high-resolution skip features; masks from points, boxes and mask logits (no video memory). The portable module() is the image encoder only.
This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: https://github.com/kleinicke/fastforks. Kernels for the kernels backend: https://huggingface.co/fastforks/kernels. Original code: https://github.com/facebookresearch/sam2 at 2b90b9f.
Task: promptable_segmentation. Tier: stable. Outputs: pred_masks, iou_scores. Backends: portable, kernels.
Input: one image plus a point, box or text prompt. Output: masks with quality scores.
Variants
Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in card.toml.
| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
sam2.1-hiera-base-plus@1 |
sam2.1_hiera_base_plus.pt | a2345aede871 |
Apache-2.0 | yes |
sam2.1-hiera-large@1 |
sam2.1_hiera_large.pt | 2647878d5dfa |
Apache-2.0 | yes |
sam2.1-hiera-small@1 |
sam2.1_hiera_small.pt | 6d1aa6f30de5 |
Apache-2.0 | yes |
sam2.1-hiera-tiny@1 |
sam2.1_hiera_tiny.pt | 7402e0d864fa |
Apache-2.0 | yes |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: LICENSE.
Training data: SA-1B and the SA-V video dataset (plus internal data) per the paper; SA-V itself is under CC BY 4.0, SA-1B under its research licence.
Licence evidence: https://github.com/facebookresearch/sam2/blob/main/README.md (License section: model checkpoints and code under Apache 2.0)
Input size
any H x W >= 1, resized inside to the fixed encoder input 1024x1024 (the default export size)
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): sam2.1-hiera-base-plus 1080x1920 (0.4 GiB peak allocated, 0.6 reserved); sam2.1-hiera-large 1080x1920 (0.7 GiB peak allocated, 0.9 reserved); sam2.1-hiera-small 1080x1920 (0.3 GiB peak allocated, 0.4 reserved); sam2.1-hiera-tiny 1080x1920 (0.3 GiB peak allocated, 0.4 reserved)
Speed and memory
Promptable segmentation, synthetic, 1024x1024
| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
|---|---|---|---|---|---|---|---|---|---|
sam2.1-hiera-base-plus |
102 | – | 32.0 | 25.1 | – | 1.3x | – | 412 / 464 | 426 / 524 |
sam2.1-hiera-large |
243 | – | 68.0 | 48.8 | – | 1.4x | – | 742 / 768 | 760 / 828 |
sam2.1-hiera-small |
68.5 | – | 18.8 | 15.6 | – | 1.2x | – | 318 / 366 | 330 / 386 |
sam2.1-hiera-tiny |
61.3 | – | 16.6 | 13.9 | – | 1.2x | – | 304 / 382 | 316 / 362 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
sam2.1-hiera-base-plus |
0.53 | 0.43 | 0.19† | 0.10 | 0.03† | 0.03† |
sam2.1-hiera-large |
2.92 | 1.10 | 0.39† | 0.24 | 0.07† | 0.05† |
sam2.1-hiera-small |
0.32 | 0.36 | 0.13† | 0.07 | 0.02† | 0.02† |
sam2.1-hiera-tiny |
0.41 | 0.25 | 0.11† | 0.13 | 0.02† | 0.01† |
torch.compile and TensorRT
| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
|---|---|---|---|---|---|---|---|---|---|---|
sam2.1-hiera-tiny |
1024x1024 | 16.6 | 14.2 | 13.4 | 8.6 | 34 | 13.9 | 304 / 382 | 270 / 322 | 266 |
Fixed-size speed and peak memory, all models
| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
|---|---|---|---|---|---|
sam2.1-hiera-base-plus |
portable | 32.0 ms / 395 / 540 | 31.9 ms / 402 / 558 | 32.1 ms / 415 / 568 | - |
sam2.1-hiera-base-plus |
kernels | 25.1 ms / 408 / 472 | 25.1 ms / 415 / 490 | 25.3 ms / 428 / 472 | - |
sam2.1-hiera-large |
portable | 68.2 ms / 724 / 936 | 68.6 ms / 732 / 948 | 68.3 ms / 745 / 954 | - |
sam2.1-hiera-large |
kernels | 48.7 ms / 742 / 824 | 48.7 ms / 750 / 836 | 49.0 ms / 763 / 842 | - |
sam2.1-hiera-small |
portable | 18.7 ms / 298 / 420 | 19.1 ms / 305 / 422 | 18.9 ms / 319 / 426 | - |
sam2.1-hiera-small |
kernels | 15.4 ms / 310 / 356 | 15.5 ms / 317 / 358 | 15.6 ms / 331 / 346 | - |
sam2.1-hiera-tiny |
portable | 16.7 ms / 285 / 410 | 16.6 ms / 292 / 410 | 16.8 ms / 305 / 426 | - |
sam2.1-hiera-tiny |
kernels | 13.8 ms / 296 / 346 | 13.8 ms / 304 / 346 | 14.0 ms / 317 / 346 | - |
Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
Hardware, protocol and caveats: benchmarks (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or (fp32) where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
Accuracy
Parity with the original code
Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 3 % and p99 <= 15 % of the reference's median |value| (absolute error of the output pred_masks).
Usage
from fastforks import promptable_segmentation
model = promptable_segmentation.load("sam2.1-hiera-base-plus") # mode="auto": the preferred backend from recorded measurements
result = model("photo.jpg", input_points=[[x, y]], input_labels=[1])
result.masks # [M, H, W] bool
# SAM 3 (text prompts): model("photo.jpg", text="coin") -> boxes, scores, masks
result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
Install with pip install fastforks (see https://github.com/kleinicke/fastforks). Other variants: sam2.1-hiera-base-plus, sam2.1-hiera-large, sam2.1-hiera-small, sam2.1-hiera-tiny.
Kernels backend
promptable_segmentation.load("sam2.1-hiera-base-plus", mode="kernels") runs our CUDA kernels (dpt_fused). It needs a prebuilt kernel for your GPU architecture (downloaded from https://huggingface.co/fastforks/kernels on first use) and a recent NVIDIA driver; if there is none it raises BackendUnavailableError with the reason and never falls back silently. mode="auto" picks kernels when it can run and logs the choice.