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library_name: "fastforks"
pipeline_tag: "image-classification"
license: "apache-2.0"
tags:
- "classification"
- "image-classification"
- "cuda"
- "pytorch"
- "fastforks"
---
# EfficientViT (MIT) (fastforks)
EfficientViT (Cai et al. 2023, MIT Han lab): MBConv stages with multi-scale ReLU linear attention.
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/efficientvit_b2.r288_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/efficientvit/card.toml).
| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
| `efficientvit_b2.r288_in1k@1` | [efficientvit_b2.r288_in1k.safetensors](https://huggingface.co/timm/efficientvit_b2.r288_in1k/resolve/891530d0c5a400dd2d4549c81d433661b192abe1/model.safetensors) | `f5726851b5c5` | 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: .
Training data: ImageNet-1k (ILSVRC 2012 classification).
Licence evidence: <https://huggingface.co/timm/efficientvit_b2.r288_in1k/blob/891530d0c5a400dd2d4549c81d433661b192abe1/README.md>
## Input size
fixed 288x288: the input is resized (short side 288) and centre-cropped to it
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `efficientvit_b2.r288_in1k` 288x288 (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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| `efficientvit_b2.r288_in1k` | 10.67 | 14.66 | 11.9 | 1.105 | 9.7x | 10.8x | 173 / 194 | 85 / 124 | 120 / 212 |
### Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
| --- | --- | --- | --- | --- | --- | --- |
| `efficientvit_b2.r288_in1k` | 1.84 | 0.40 | 1.92 | 1.89 | 0.04 | 0.05 |
### 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) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| `efficientvit_b2.r288_in1k` | 224x224 | 11.9 | 6.9 | 11.0 | 0.7 | 39 | 1.105 | 85 / 124 | 120 / 164 | 72 |
### Fixed-size speed and peak memory, all models
| model | mode | 224x224 |
| --- | --- | --- |
| `efficientvit_b2.r288_in1k` | portable | 11.9 ms / 87 / 116 |
| `efficientvit_b2.r288_in1k` | kernels | 1.2 ms / 122 / 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 golden 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| <= 0.05 and p99 <= 0.2 (absolute error of the output `logits`).
## Usage
```python
from fastforks import classification
model = classification.load("efficientvit_b2.r288_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: `efficientvit_b2.r288_in1k`.
## Kernels backend
`classification.load("efficientvit_b2.r288_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.
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