Models · Classification

EfficientNet

EfficientNet (Tan and Le 2019): compound-scaled classifiers of different sizes.

Commercial use: yes.

variantinputbackendtime (ms)peak memory (MiB, allocated / reserved)
efficientnet_b0.tv_in1ksynthetic (224x224)original (fp32)
original
portable
kernels
portable + torch.compile
TensorRT fp16		5.64
7.91
4.98
0.769
2.88
0.536		64 / 78
64 / 84
48 / 96
78 / 118
80 / 96
38 (engine)
efficientnet_b4.tv_in1ksynthetic (224x224)original (fp32)
original
portable
kernels		11.1
15.7
10.0
2.00		156 / 190
147 / 174
89 / 114
111 / 180
Details
  • Original code: https://github.com/pytorch/vision at 1f1a920d685d
  • Code licence: BSD-3-Clause (permissive); files:
  • Weights licence: BSD-3-Clause (assumed from code)
  • Licence evidence: https://github.com/pytorch/vision/blob/1f1a920d685d46dfbe187934b785b7ecb7a182f2/LICENSE
  • Training data: ImageNet-1k (ILSVRC 2012 classification)
  • Kernel libraries: cudnn_hacc, fvm_conv, fvm_extra, fvm_mb, fvm_r3
  • Variants: efficientnet_b0.tv_in1k (default of efficientnet_b0), efficientnet_b4.tv_in1k (default of efficientnet_b4)
  • Outputs: logits; label_set: imagenet-1k
  • 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)
  • efficientnet_b0.tv_in1k 224x224, first call: portable + torch.compile 9 s (cold Inductor cache), TensorRT fp16 26 s (ONNX + engine).
  • efficientnet_b0.tv_in1k original 224x224: torch.autocast(fp16) around the forward
  • efficientnet_b4.tv_in1k original 224x224: torch.autocast(fp16) around the forward
  • efficientnet_b0.tv_in1k load / first call: original 1.25 s / 2.863 s, portable 0.17 s / 0.378 s, kernels 1.53 s / 0.063 s
  • efficientnet_b4.tv_in1k load / first call: original 0.41 s / 1.72 s, portable 0.37 s / 0.061 s, kernels 1.94 s / 0.065 s
  • Protocol, median of 9 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.

variantdatasetoriginal fp32original fp16portablekernelsnote
efficientnet_b0.tv_in1kImageNet-1k val, 5000 random images, top-1 / top-5 %78.62 / 93.8678.62 / 93.8678.60 / 93.8678.56 / 93.88
efficientnet_b4.tv_in1kImageNet-1k val, 5000 random images, top-1 / top-5 %84.04 / 96.8284.02 / 96.8684.02 / 96.8684.02 / 96.86

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).

variantkernels error vs original, mean / p99GPU, torchkernels used
efficientnet_b0.tv_in1k0.00343 / 0.01174RTX 5060 Ti, torch 2.14.1+cu130cudnn_hacc@cudnn92400, fvm_conv@f1d064e539e1:sm_120, fvm_extra@3208ce525a91:sm_120, fvm_mb@720dd3f29bf7:sm_120, fvm_r3@f54eb890e23c:sm_120
efficientnet_b4.tv_in1k0.00234 / 0.00886RTX 5060 Ti, torch 2.14.1+cu130cudnn_hacc@cudnn92400, fvm_conv@f1d064e539e1:sm_120, fvm_extra@3208ce525a91:sm_120, fvm_mb@720dd3f29bf7:sm_120, fvm_r3@f54eb890e23c:sm_120