Vision Transformer
Vision Transformer (Dosovitskiy et al. 2020): a transformer classifier on 16x16 image patches.
Commercial use: yes.
| variant | input | backend | time (ms) | peak memory (MiB, allocated / reserved) |
|---|---|---|---|---|
vit_base_patch16_224.augreg2_in21k_ft_in1k | synthetic (224x224) | original (fp32) original portable kernels portable + torch.compile TensorRT fp16 | 6.33 5.48 3.02 1.65 2.10 1.14 | 371 / 386 531 / 548 205 / 390 300 / 370 238 / 266 196 (engine) |
Details
- Original code: https://github.com/huggingface/pytorch-image-models at
0df212b369a5 - Code licence: Apache-2.0 (permissive); files:
- Weights licence: Apache-2.0
- Licence evidence: https://huggingface.co/timm/vit_base_patch16_224.augreg2_in21k_ft_in1k/blob/063c6c38a5d8510b2e57df480445e94b231dad2c/README.md
- Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ImageNet-1k (ILSVRC 2012 classification)
- Kernel libraries:
cudnn_hacc,fvm_conv,fvm_extra,fvm_mb,fvm_r3 - Variants:
vit_base_patch16_224.augreg2_in21k_ft_in1k(default ofvit_base_patch16_224) - Outputs: logits; label_set:
imagenet-1k - Input size: fixed 224x224: the input is resized (short side 248) and centre-cropped to it
- Tested up to (portable, fp16, RTX 5060 Ti 16 GB):
vit_base_patch16_224.augreg2_in21k_ft_in1k224x224 (0.2 GiB peak allocated, 0.3 reserved) vit_base_patch16_224.augreg2_in21k_ft_in1k224x224, first call: portable + torch.compile 7 s (cold Inductor cache), TensorRT fp16 29 s (ONNX + engine).vit_base_patch16_224.augreg2_in21k_ft_in1koriginal 224x224: torch.autocast(fp16) around the forwardvit_base_patch16_224.augreg2_in21k_ft_in1kload / first call: original 2.49 s / 0.283 s, portable 0.73 s / 0.004 s, kernels 2.05 s / 0.069 s- Protocol, median of 5 after 3 warm-up, uint8 CUDA input (synthetic for bench_inputs sizes), card defaults; gc before each load; peak = max over the first call and the timed calls
Measured
fastforks: the model's own resize / crop on the GPU; original: the library's pretrained loader and its PIL eval transform. Original = the original code named under Details, same checkpoint and protocol.
| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
|---|---|---|---|---|---|---|
vit_base_patch16_224.augreg2_in21k_ft_in1k | ImageNet-1k val, 5000 random images, top-1 / top-5 % | 85.88 / 97.70 | 85.90 / 97.70 | 85.84 / 97.70 | 85.88 / 97.68 |
Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 939adfe8, 2026-11-06.
Parity
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).
| variant | kernels error vs original, mean / p99 | GPU, torch | kernels used |
|---|---|---|---|
vit_base_patch16_224.augreg2_in21k_ft_in1k | 0.00273 / 0.01018 | RTX 5060 Ti, torch 2.14.1+cu130 | cudnn_hacc@cudnn92400, fvm_conv@f1d064e539e1:sm_120, fvm_extra@3208ce525a91:sm_120, fvm_mb@720dd3f29bf7:sm_120, fvm_r3@f54eb890e23c:sm_120 |