fastforks 0.1.0

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README.md

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+---
+library_name: "fastforks"
+pipeline_tag: "image-classification"
+license: "other"
+license_name: "bsd-3-clause-code-weights-unstated"
+license_link: "https://github.com/pytorch/vision/blob/1f1a920d685d46dfbe187934b785b7ecb7a182f2/LICENSE"
+tags:
+- "classification"
+- "image-classification"
+- "cuda"
+- "pytorch"
+- "fastforks"
+---
+# EfficientNet (fastforks)
+
+EfficientNet (Tan and Le 2019): compound-scaled MBConv networks with squeeze-excite.
+
+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/pytorch/vision> at `1f1a920d685d`.
+
+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/efficientnet/card.toml).
+
+| variant | weights (author link) | SHA-256 | weights licence | commercial use |
+|---|---|---|---|---|
+| `efficientnet_b0.tv_in1k@1` | [efficientnet_b0_rwightman-7f5810bc.pth](https://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pth) | `7f5810bc96de` | BSD-3-Clause (assumed from code) | yes |
+| `efficientnet_b4.tv_in1k@1` | [efficientnet_b4_rwightman-23ab8bcd.pth](https://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pth) | `23ab8bcd5bdb` | BSD-3-Clause (assumed from code) | yes |
+
+## Licence
+
+Original-code strings, recorded as they are (research-use package; check them before any other use). Code: BSD-3-Clause. Files: .
+
+Code licence: BSD-3-Clause; the authors do not state a separate weights licence.
+
+Training data: ImageNet-1k (ILSVRC 2012 classification).
+
+Licence evidence: <https://github.com/pytorch/vision/blob/1f1a920d685d46dfbe187934b785b7ecb7a182f2/LICENSE>
+
+## Input size
+
+- `efficientnet_b0.tv_in1k`: fixed 224x224: the input is resized (short side 256) and centre-cropped to it
+- `efficientnet_b4.tv_in1k`: fixed 380x380: the input is resized (short side 384) and centre-cropped to it
+Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `efficientnet_b0.tv_in1k` 224x224 (0.0 GiB peak allocated, 0.1 reserved); `efficientnet_b4.tv_in1k` 380x380 (0.1 GiB peak allocated, 0.1 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 |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `efficientnet_b0.tv_in1k` | 5.64 | 7.91 | 4.98 | 0.769 | 7.3x | 6.5x | 64 / 84 | 48 / 96 | 78 / 118 |
+| `efficientnet_b4.tv_in1k` | 11.1 | 15.7 | 10.0 | 2.00 | 5.5x | 5.0x | 147 / 174 | 89 / 114 | 111 / 180 |
+
+### Startup and first call
+
+| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- |
+| `efficientnet_b0.tv_in1k` | 1.25 | 0.17 | 1.53 | 2.86 | 0.38 | 0.06 |
+| `efficientnet_b4.tv_in1k` | 0.41 | 0.37 | 1.94 | 1.72 | 0.06 | 0.07 |
+
+### 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) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `efficientnet_b0.tv_in1k` | 224x224 | 4.98 | 2.9 | 9.1 | 0.5 | 26 | 0.769 | 48 / 96 | 80 / 96 | 38 |
+
+### Fixed-size speed and peak memory, all models
+
+| model | mode | 224x224 |
+| --- | --- | --- |
+| `efficientnet_b0.tv_in1k` | portable | 5.0 ms / 48 / 84 |
+| `efficientnet_b0.tv_in1k` | kernels | 0.9 ms / 81 / 164 |
+| `efficientnet_b4.tv_in1k` | portable | 9.9 ms / 92 / 120 |
+| `efficientnet_b4.tv_in1k` | kernels | 2.2 ms / 125 / 200 |
+
+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
+
+model = classification.load("efficientnet_b0.tv_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: `efficientnet_b0.tv_in1k`, `efficientnet_b4.tv_in1k`.
+
+## Kernels backend
+
+`classification.load("efficientnet_b0.tv_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) -->