| --- |
| library_name: "fastforks" |
| pipeline_tag: "image-classification" |
| license: "cc-by-nc-4.0" |
| tags: |
| - "classification" |
| - "image-classification" |
| - "cuda" |
| - "pytorch" |
| - "fastforks" |
| --- |
| # ConvNeXt V2 (fastforks) |
|
|
| ConvNeXt V2 (Woo et al. 2023): ConvNeXt blocks with global response normalisation, FCMAE pre-training. |
|
|
| 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/huggingface/pytorch-image-models> at `0df212b369a5`. |
|
|
| Author weight repos: <https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k>. |
|
|
| Task: classification. Tier: stable. Outputs: `logits`. Backends: `portable`, `kernels`. |
|
|
| Input: one RGB image. Output: ImageNet-1k class logits. |
|
|
| ## Variants |
|
|
| Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/classification/models/convnextv2/card.toml). |
|
|
| | variant | weights (author link) | SHA-256 | weights licence | commercial use | |
| |---|---|---|---|---| |
| | `convnextv2_base.fcmae_ft_in22k_in1k@1` | [convnextv2_base.fcmae_ft_in22k_in1k.safetensors](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k/resolve/9e250ed8f88b436472d2b24af26f82a8aa8c719d/model.safetensors) | `3b04d2fd7f52` | CC-BY-NC-4.0 | **no** | |
|
|
| ## Licence |
|
|
| Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: . |
|
|
| Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ImageNet-1k (ILSVRC 2012 classification). |
|
|
| Licence evidence: <https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k/blob/9e250ed8f88b436472d2b24af26f82a8aa8c719d/README.md> |
|
|
| ## Input size |
|
|
| fixed 224x224: the input is resized (short side 256) and centre-cropped to it |
| Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `convnextv2_base.fcmae_ft_in22k_in1k` 224x224 (0.3 GiB peak allocated, 0.4 reserved) |
|
|
| ## Speed and memory |
|
|
| ### Classification, synthetic, 224x224 |
|
|
| | model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | `convnextv2_base.fcmae_ft_in22k_in1k` | 10.7 | 14.6 | 10.2 | 2.94 | 3.7x | 3.5x | 542 / 566 | 299 / 376 | 318 / 380 | |
|
|
| ### Startup and first call |
|
|
| | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `convnextv2_base.fcmae_ft_in22k_in1k` | 2.70 | 1.23 | 2.12 | 0.63 | 0.03 | 0.16 | |
|
|
| ### 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) | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | `convnextv2_base.fcmae_ft_in22k_in1k` | 224x224 | 10.2 | 3.8 | 14.4 | 2.1 | 35 | 2.94 | 299 / 376 | 324 / 356 | 204 | |
|
|
| ### Fixed-size speed and peak memory, all models |
|
|
| | model | mode | 224x224 | |
| | --- | --- | --- | |
| | `convnextv2_base.fcmae_ft_in22k_in1k` | portable | 10.2 ms / 300 / 370 | |
| | `convnextv2_base.fcmae_ft_in22k_in1k` | kernels | 3.0 ms / 334 / 450 | |
|
|
| Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB). |
|
|
| Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (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| <= 0.02 and p99 <= 0.08 (absolute error of the output `logits`). |
| |
| ## Usage |
| |
| ```python |
| from fastforks import classification |
| |
| # non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment |
| model = classification.load("convnextv2_base.fcmae_ft_in22k_in1k") # mode="auto": the preferred backend from recorded measurements |
| result = model("photo.jpg") |
| result.top(5) # top-5 classes |
| 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: `convnextv2_base.fcmae_ft_in22k_in1k`. |
| |
| ## Kernels backend |
| |
| `classification.load("convnextv2_base.fcmae_ft_in22k_in1k", mode="kernels")` runs our CUDA kernels (`cudnn_hacc`, `fvm_conv`, `fvm_extra`, `fvm_mb`, `fvm_r3`). 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. |
| |
| <!-- staged from fastforks feccac41 (2026-11-06) --> |
| |
