Embedl-optimized
- timm/lcnet_075.ra2_in1k
- INT8
- L4
- Labels
- Image
- Classification
- Build
- Embedl-optimized
- Precision
- INT8
- Runtime
- TensorRT 11.2.1.2 with Embedl optimizations
- Input size
- 224×224
lcnet_075.ra2_in1k is an image classifier from timm. It labels an image with one of the 1,000 ImageNet classes.
Methodology
- Latency: trtexec on the NVIDIA L4, batch 1, 201 iterations after warm-up, during the build: GPU compute time per inference, mean and p99, and throughput.
- Accuracy: the INT8 engine on the full ImageNet validation set (50,000 images), top-1 and top-5. timm publishes 68.786% top-1 for the FP32 weights (https://github.com/huggingface/pytorch-image-models/blob/v1.0.30/results/results-imagenet.csv); the INT8 engine is -0.05 points from it.
- Quantization: Embedl Deploy, activations INT8, weights INT8 per-channel; calibrated on ImageNet (5,000 images). Exported as ONNX opset 20, compiled with TensorRT 11.2.1.2, built on NVIDIA L4 (sm_89).
- Build: config lcnet_075/ra2_in1k-timm_int8_embedl_tensorrt_l4, on an NVIDIA L4.