How to deploy YOLO11n-pose
In this tutorial, we will introduce how to quantize a pre-trained YOLO11n-pose model using ESP-PPQ and deploy the quantized YOLO11n-pose model using ESP-DL.
Preparation
Model quantization
Pre-trained Model
You can download pre-trained yolo11n-pose model from Ultralytics release.
Currently, ESP-PPQ supports ONNX, PyTorch, and TensorFlow models. During the quantization process, PyTorch and TensorFlow models are first converted to ONNX models, so the pre-trained yolo11n-pose model needs to be converted to an ONNX model.
Specifically, refer to the script export_onnx.py to convert the pre-trained yolo11n-pose model to an ONNX model.
In the script, we have overridden the forward method of the Pose class, which offers following advantages:
Faster inference. Compared to the original yolo11n-pose model, operations related to decoding bounding boxes and keypoints in Pose head are moved from the inference pass to the post-processing phase, resulting in a significant reduction in inference latency. On one hand, operations like
Conv,Transpose,Slice,SplitandConcatare time-consuming when applied during inference pass. On the other hand, the inference outputs are first filtered using a score threshold before decoding the boxes in the post-processing pass, which significantly reduces the number of calculations, thereby accelerating the overall inference speed.Lower quantization Error. The
ConcatandAddoperators adopt joint quantization in ESP-PPQ. To reduce quantization errors, the box and score are output by separate branches, rather than being concatenated, due to the significant difference in their ranges. Similarly, since the ranges of the two inputs ofAddandSubdiffer significantly, the calculations are performed in the post-processing phase to avoid quantization errors.
Calibration Dataset
The calibration dataset needs to match the input format of the model. The calibration dataset should cover all possible input scenarios to better quantize the model. Here, the calibration dataset used in this example is calib_yolo11n-pose.
8bit Post-Training Quantization
The following quantization settings were generated by AutoQuant. To use AutoQuant, please update esp-ppq to the latest version and refer to the tutorial.
ESP32-P4 Quantization settings
quant_setting = QuantizationSettingFactory.espdl_setting()
quant_setting.quantize_activation_setting.calib_algorithm = 'percentile'
quant_setting.bias_correct = True
quant_setting.bias_correct_setting.interested_layers = []
quant_setting.bias_correct_setting.block_size = 2
quant_setting.bias_correct_setting.steps = 32
ESP32-P4 Quantization error
Analysing Graphwise Quantization Error(Phrase 1):: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.03it/s]
Analysing Graphwise Quantization Error(Phrase 2):: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:06<00:00, 3.00s/it]
Layer | NOISE:SIGNAL POWER RATIO
/model.22/m.0/cv2/conv/Conv: | ████████████████████ | 9.927%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | ███████████████████ | 9.484%
/model.23/cv4.1/cv4.1.0/conv/Conv: | ██████████████████ | 9.145%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | █████████████████ | 8.567%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | █████████████████ | 8.372%
/model.20/conv/Conv: | █████████████████ | 8.366%
/model.23/cv2.0/cv2.0.1/conv/Conv: | ████████████████ | 7.941%
/model.19/m.0/cv2/conv/Conv: | ████████████████ | 7.861%
/model.23/cv4.1/cv4.1.1/conv/Conv: | ████████████████ | 7.778%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | ████████████████ | 7.773%
/model.23/cv4.0/cv4.0.0/conv/Conv: | ████████████████ | 7.769%
/model.23/cv4.0/cv4.0.1/conv/Conv: | ████████████████ | 7.749%
/model.23/cv2.1/cv2.1.1/conv/Conv: | ████████████████ | 7.704%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | ███████████████ | 7.641%
/model.22/m.0/cv3/conv/Conv: | ███████████████ | 7.541%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | ███████████████ | 7.432%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | ███████████████ | 7.315%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | ██████████████ | 7.086%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | ██████████████ | 7.036%
/model.22/cv1/conv/Conv: | █████████████ | 6.485%
/model.19/cv2/conv/Conv: | █████████████ | 6.333%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | █████████████ | 6.296%
/model.22/m.0/m/m.1/cv2/conv/Conv: | █████████████ | 6.255%
/model.22/cv2/conv/Conv: | ████████████ | 6.196%
/model.17/conv/Conv: | ████████████ | 6.110%
/model.23/cv2.2/cv2.2.0/conv/Conv: | ████████████ | 5.841%
/model.23/cv4.2/cv4.2.1/conv/Conv: | ████████████ | 5.763%
/model.23/cv4.2/cv4.2.0/conv/Conv: | ████████████ | 5.740%
/model.23/cv2.1/cv2.1.0/conv/Conv: | ███████████ | 5.657%
/model.19/cv1/conv/Conv: | ███████████ | 5.583%
/model.23/cv2.2/cv2.2.1/conv/Conv: | ███████████ | 5.552%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | ███████████ | 5.254%
/model.6/m.0/cv2/conv/Conv: | ███████████ | 5.245%
/model.13/m.0/cv2/conv/Conv: | ██████████ | 5.172%
/model.19/m.0/cv1/conv/Conv: | ██████████ | 5.166%
/model.22/m.0/m/m.0/cv2/conv/Conv: | ██████████ | 5.136%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | ██████████ | 5.061%
/model.8/m.0/cv2/conv/Conv: | ██████████ | 4.985%
/model.10/m/m.0/attn/proj/conv/Conv: | ██████████ | 4.962%
/model.22/m.0/m/m.0/cv1/conv/Conv: | █████████ | 4.609%
/model.23/cv4.2/cv4.2.2/Conv: | █████████ | 4.572%
/model.22/m.0/m/m.1/cv1/conv/Conv: | █████████ | 4.417%
/model.16/m.0/cv2/conv/Conv: | █████████ | 4.411%
/model.6/cv1/conv/Conv: | █████████ | 4.264%
/model.23/cv4.1/cv4.1.2/Conv: | █████████ | 4.264%
/model.10/m/m.0/attn/pe/conv/Conv: | ████████ | 4.161%
/model.23/cv2.0/cv2.0.0/conv/Conv: | ████████ | 4.063%
/model.3/conv/Conv: | ████████ | 3.764%
/model.16/cv2/conv/Conv: | ███████ | 3.694%
/model.8/cv1/conv/Conv: | ███████ | 3.689%
/model.13/cv2/conv/Conv: | ███████ | 3.543%
/model.23/cv4.0/cv4.0.2/Conv: | ███████ | 3.376%
/model.22/m.0/cv1/conv/Conv: | ███████ | 3.363%
/model.10/cv1/conv/Conv: | ███████ | 3.357%
/model.8/cv2/conv/Conv: | ███████ | 3.279%
/model.6/m.0/cv3/conv/Conv: | ██████ | 3.254%
/model.8/m.0/cv3/conv/Conv: | ██████ | 3.219%
/model.13/cv1/conv/Conv: | ██████ | 3.180%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | ██████ | 3.142%
/model.13/m.0/cv1/conv/Conv: | ██████ | 3.129%
/model.10/m/m.0/attn/qkv/conv/Conv: | ██████ | 3.074%
/model.16/m.0/cv1/conv/Conv: | ██████ | 3.061%
/model.2/m.0/cv2/conv/Conv: | ██████ | 3.024%
/model.4/cv1/conv/Conv: | ██████ | 2.990%
/model.6/m.0/m/m.0/cv2/conv/Conv: | ██████ | 2.844%
/model.16/cv1/conv/Conv: | ██████ | 2.821%
/model.8/m.0/m/m.1/cv2/conv/Conv: | ██████ | 2.807%
/model.4/cv2/conv/Conv: | ██████ | 2.781%
/model.4/m.0/cv1/conv/Conv: | █████ | 2.742%
/model.10/cv2/conv/Conv: | █████ | 2.627%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | █████ | 2.613%
/model.2/cv2/conv/Conv: | █████ | 2.611%
/model.6/cv2/conv/Conv: | █████ | 2.593%
/model.8/m.0/cv1/conv/Conv: | █████ | 2.553%
/model.10/m/m.0/attn/MatMul_1: | █████ | 2.547%
/model.7/conv/Conv: | █████ | 2.447%
/model.5/conv/Conv: | █████ | 2.433%
/model.10/m/m.0/attn/MatMul: | █████ | 2.363%
/model.23/cv2.1/cv2.1.2/Conv: | █████ | 2.344%
/model.6/m.0/m/m.0/cv1/conv/Conv: | █████ | 2.305%
/model.6/m.0/cv1/conv/Conv: | ████ | 2.250%
/model.8/m.0/m/m.0/cv2/conv/Conv: | ████ | 2.247%
/model.2/cv1/conv/Conv: | ████ | 2.080%
/model.8/m.0/m/m.1/cv1/conv/Conv: | ████ | 2.070%
/model.23/cv2.2/cv2.2.2/Conv: | ████ | 1.977%
/model.6/m.0/m/m.1/cv1/conv/Conv: | ████ | 1.927%
/model.9/cv1/conv/Conv: | ████ | 1.926%
/model.23/cv2.0/cv2.0.2/Conv: | ████ | 1.859%
/model.8/m.0/m/m.0/cv1/conv/Conv: | ███ | 1.694%
/model.9/cv2/conv/Conv: | ███ | 1.672%
/model.23/cv3.2/cv3.2.2/Conv: | ███ | 1.499%
/model.4/m.0/cv2/conv/Conv: | ███ | 1.491%
/model.6/m.0/m/m.1/cv2/conv/Conv: | ███ | 1.452%
/model.2/m.0/cv1/conv/Conv: | ██ | 1.093%
/model.1/conv/Conv: | ██ | 0.834%
/model.23/cv3.1/cv3.1.2/Conv: | █ | 0.568%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.128%
/model.0/conv/Conv: | | 0.046%
Analysing Layerwise quantization error:: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 98/98 [04:07<00:00, 2.53s/it]
Layer | NOISE:SIGNAL POWER RATIO
/model.0/conv/Conv: | ████████████████████ | 0.323%
/model.2/cv1/conv/Conv: | ██████████ | 0.155%
/model.1/conv/Conv: | ████████ | 0.128%
/model.2/cv2/conv/Conv: | ██████ | 0.104%
/model.8/cv1/conv/Conv: | ████ | 0.070%
/model.9/cv2/conv/Conv: | ███ | 0.049%
/model.6/m.0/m/m.0/cv2/conv/Conv: | █ | 0.016%
/model.3/conv/Conv: | █ | 0.014%
/model.2/m.0/cv1/conv/Conv: | █ | 0.014%
/model.2/m.0/cv2/conv/Conv: | █ | 0.014%
/model.6/m.0/m/m.1/cv2/conv/Conv: | █ | 0.012%
/model.23/cv4.1/cv4.1.1/conv/Conv: | █ | 0.011%
/model.5/conv/Conv: | █ | 0.010%
/model.9/cv1/conv/Conv: | █ | 0.010%
/model.6/cv1/conv/Conv: | █ | 0.010%
/model.4/cv2/conv/Conv: | █ | 0.009%
/model.4/cv1/conv/Conv: | █ | 0.009%
/model.16/m.0/cv2/conv/Conv: | █ | 0.009%
/model.19/m.0/cv2/conv/Conv: | █ | 0.009%
/model.10/m/m.0/attn/qkv/conv/Conv: | █ | 0.008%
/model.10/cv1/conv/Conv: | | 0.008%
/model.23/cv4.2/cv4.2.0/conv/Conv: | | 0.008%
/model.13/m.0/cv1/conv/Conv: | | 0.008%
/model.13/cv1/conv/Conv: | | 0.007%
/model.8/m.0/cv3/conv/Conv: | | 0.007%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | | 0.007%
/model.23/cv2.0/cv2.0.2/Conv: | | 0.007%
/model.8/m.0/m/m.1/cv1/conv/Conv: | | 0.007%
/model.22/cv2/conv/Conv: | | 0.007%
/model.6/cv2/conv/Conv: | | 0.006%
/model.23/cv4.2/cv4.2.2/Conv: | | 0.006%
/model.16/cv2/conv/Conv: | | 0.006%
/model.23/cv4.1/cv4.1.0/conv/Conv: | | 0.006%
/model.23/cv4.2/cv4.2.1/conv/Conv: | | 0.006%
/model.8/cv2/conv/Conv: | | 0.006%
/model.16/cv1/conv/Conv: | | 0.006%
/model.13/cv2/conv/Conv: | | 0.006%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | | 0.005%
/model.13/m.0/cv2/conv/Conv: | | 0.005%
/model.7/conv/Conv: | | 0.005%
/model.10/cv2/conv/Conv: | | 0.005%
/model.22/m.0/m/m.0/cv2/conv/Conv: | | 0.005%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | | 0.005%
/model.23/cv2.1/cv2.1.2/Conv: | | 0.005%
/model.8/m.0/m/m.1/cv2/conv/Conv: | | 0.005%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | | 0.005%
/model.19/cv2/conv/Conv: | | 0.004%
/model.4/m.0/cv2/conv/Conv: | | 0.004%
/model.8/m.0/cv1/conv/Conv: | | 0.004%
/model.23/cv2.2/cv2.2.1/conv/Conv: | | 0.004%
/model.19/cv1/conv/Conv: | | 0.004%
/model.23/cv2.0/cv2.0.1/conv/Conv: | | 0.004%
/model.10/m/m.0/attn/pe/conv/Conv: | | 0.004%
/model.23/cv2.2/cv2.2.2/Conv: | | 0.004%
/model.22/m.0/m/m.1/cv2/conv/Conv: | | 0.004%
/model.23/cv4.0/cv4.0.0/conv/Conv: | | 0.004%
/model.19/m.0/cv1/conv/Conv: | | 0.003%
/model.10/m/m.0/attn/proj/conv/Conv: | | 0.003%
/model.22/m.0/cv3/conv/Conv: | | 0.003%
/model.8/m.0/m/m.0/cv1/conv/Conv: | | 0.003%
/model.23/cv2.1/cv2.1.0/conv/Conv: | | 0.003%
/model.23/cv3.2/cv3.2.2/Conv: | | 0.002%
/model.10/m/m.0/attn/MatMul_1: | | 0.002%
/model.4/m.0/cv1/conv/Conv: | | 0.002%
/model.23/cv4.1/cv4.1.2/Conv: | | 0.002%
/model.22/m.0/cv1/conv/Conv: | | 0.002%
/model.8/m.0/m/m.0/cv2/conv/Conv: | | 0.002%
/model.22/cv1/conv/Conv: | | 0.002%
/model.23/cv4.0/cv4.0.2/Conv: | | 0.002%
/model.22/m.0/m/m.0/cv1/conv/Conv: | | 0.002%
/model.22/m.0/m/m.1/cv1/conv/Conv: | | 0.002%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | | 0.002%
/model.23/cv4.0/cv4.0.1/conv/Conv: | | 0.002%
/model.16/m.0/cv1/conv/Conv: | | 0.002%
/model.23/cv2.1/cv2.1.1/conv/Conv: | | 0.002%
/model.6/m.0/cv1/conv/Conv: | | 0.002%
/model.6/m.0/cv3/conv/Conv: | | 0.002%
/model.23/cv2.0/cv2.0.0/conv/Conv: | | 0.002%
/model.6/m.0/m/m.1/cv1/conv/Conv: | | 0.002%
/model.6/m.0/m/m.0/cv1/conv/Conv: | | 0.001%
/model.23/cv2.2/cv2.2.0/conv/Conv: | | 0.001%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | | 0.001%
/model.17/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | | 0.001%
/model.20/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.2/Conv: | | 0.000%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | | 0.000%
/model.6/m.0/cv2/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | | 0.000%
/model.8/m.0/cv2/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | | 0.000%
/model.10/m/m.0/attn/MatMul: | | 0.000%
/model.22/m.0/cv2/conv/Conv: | | 0.000%
ESP32-P4 Quantization results
With the same inputs, The Pose mAP50:95 on COCO after quantization is 43.9%, which is lower than that of the float model（50.0%）.
Quantization-Aware Training
To further improve the accuracy of the quantized model, we adopt the quantization-aware training(QAT) strategy. Here, QAT is performed based on 8-bit quantization.
Quantization settings
ESP32-P4 Quantization error
Layer | NOISE:SIGNAL POWER RATIO
/model.22/m.0/cv2/conv/Conv: | ████████████████████ | 27.739%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | ███████████████████ | 26.872%
/model.23/cv4.1/cv4.1.0/conv/Conv: | ███████████████████ | 26.229%
/model.23/cv2.1/cv2.1.1/conv/Conv: | ██████████████████ | 25.300%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | ██████████████████ | 24.625%
/model.23/cv2.0/cv2.0.1/conv/Conv: | █████████████████ | 23.751%
/model.20/conv/Conv: | █████████████████ | 23.320%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | █████████████████ | 22.901%
/model.23/cv4.1/cv4.1.1/conv/Conv: | ████████████████ | 22.516%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | ████████████████ | 22.035%
/model.19/m.0/cv2/conv/Conv: | ████████████████ | 21.569%
/model.23/cv4.0/cv4.0.0/conv/Conv: | ███████████████ | 21.199%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | ███████████████ | 20.785%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | ███████████████ | 20.597%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | ███████████████ | 20.329%
/model.23/cv4.0/cv4.0.1/conv/Conv: | ███████████████ | 20.179%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | ██████████████ | 19.983%
/model.22/m.0/cv3/conv/Conv: | ██████████████ | 19.919%
/model.13/m.0/cv2/conv/Conv: | ██████████████ | 19.424%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | ██████████████ | 18.893%
/model.19/cv2/conv/Conv: | █████████████ | 18.055%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | █████████████ | 17.915%
/model.22/m.0/m/m.1/cv2/conv/Conv: | █████████████ | 17.796%
/model.22/cv1/conv/Conv: | █████████████ | 17.777%
/model.23/cv4.2/cv4.2.1/conv/Conv: | █████████████ | 17.573%
/model.19/cv1/conv/Conv: | ████████████ | 17.116%
/model.17/conv/Conv: | ████████████ | 16.869%
/model.22/cv2/conv/Conv: | ████████████ | 16.750%
/model.23/cv2.2/cv2.2.1/conv/Conv: | ████████████ | 16.540%
/model.10/m/m.0/attn/proj/conv/Conv: | ████████████ | 16.491%
/model.23/cv2.2/cv2.2.0/conv/Conv: | ████████████ | 16.421%
/model.23/cv2.1/cv2.1.0/conv/Conv: | ████████████ | 16.205%
/model.23/cv4.2/cv4.2.0/conv/Conv: | ████████████ | 16.116%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | ███████████ | 15.400%
/model.22/m.0/m/m.0/cv2/conv/Conv: | ███████████ | 15.251%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | ███████████ | 14.851%
/model.10/m/m.0/attn/pe/conv/Conv: | ███████████ | 14.659%
/model.19/m.0/cv1/conv/Conv: | ██████████ | 14.289%
/model.22/m.0/m/m.1/cv1/conv/Conv: | █████████ | 13.038%
/model.16/m.0/cv2/conv/Conv: | █████████ | 12.941%
/model.22/m.0/m/m.0/cv1/conv/Conv: | █████████ | 12.791%
/model.23/cv4.2/cv4.2.2/Conv: | █████████ | 12.508%
/model.23/cv4.1/cv4.1.2/Conv: | █████████ | 12.226%
/model.13/cv1/conv/Conv: | ████████ | 11.821%
/model.13/cv2/conv/Conv: | ████████ | 11.612%
/model.13/m.0/cv1/conv/Conv: | ████████ | 11.515%
/model.10/m/m.0/attn/MatMul_1: | ████████ | 11.303%
/model.16/cv2/conv/Conv: | ████████ | 11.028%
/model.10/m/m.0/attn/qkv/conv/Conv: | ████████ | 10.951%
/model.10/cv1/conv/Conv: | ████████ | 10.755%
/model.23/cv2.0/cv2.0.0/conv/Conv: | ████████ | 10.684%
/model.22/m.0/cv1/conv/Conv: | ███████ | 10.164%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | ███████ | 9.968%
/model.16/m.0/cv1/conv/Conv: | ███████ | 9.656%
/model.23/cv4.0/cv4.0.2/Conv: | ███████ | 9.566%
/model.8/m.0/cv2/conv/Conv: | ███████ | 9.521%
/model.10/cv2/conv/Conv: | ██████ | 8.068%
/model.16/cv1/conv/Conv: | ██████ | 7.989%
/model.23/cv2.1/cv2.1.2/Conv: | ██████ | 7.969%
/model.8/m.0/cv3/conv/Conv: | ██████ | 7.725%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | █████ | 7.570%
/model.8/m.0/m/m.0/cv2/conv/Conv: | █████ | 7.339%
/model.8/m.0/m/m.1/cv2/conv/Conv: | █████ | 7.283%
/model.8/cv2/conv/Conv: | █████ | 7.092%
/model.10/m/m.0/attn/MatMul: | █████ | 6.654%
/model.8/cv1/conv/Conv: | █████ | 6.492%
/model.8/m.0/m/m.1/cv1/conv/Conv: | █████ | 6.451%
/model.23/cv2.0/cv2.0.2/Conv: | ████ | 5.990%
/model.23/cv2.2/cv2.2.2/Conv: | ████ | 5.902%
/model.6/m.0/m/m.0/cv2/conv/Conv: | ████ | 5.898%
/model.6/m.0/cv2/conv/Conv: | ████ | 5.881%
/model.6/m.0/cv3/conv/Conv: | ████ | 5.402%
/model.8/m.0/cv1/conv/Conv: | ████ | 5.210%
/model.23/cv3.2/cv3.2.2/Conv: | ████ | 5.126%
/model.6/cv1/conv/Conv: | ████ | 4.983%
/model.9/cv2/conv/Conv: | ███ | 4.616%
/model.9/cv1/conv/Conv: | ███ | 3.934%
/model.7/conv/Conv: | ███ | 3.906%
/model.3/conv/Conv: | ███ | 3.654%
/model.6/cv2/conv/Conv: | ██ | 3.429%
/model.8/m.0/m/m.0/cv1/conv/Conv: | ██ | 3.319%
/model.2/cv2/conv/Conv: | ██ | 3.220%
/model.6/m.0/m/m.1/cv1/conv/Conv: | ██ | 3.191%
/model.6/m.0/m/m.0/cv1/conv/Conv: | ██ | 3.157%
/model.4/cv1/conv/Conv: | ██ | 2.893%
/model.6/m.0/m/m.1/cv2/conv/Conv: | ██ | 2.792%
/model.6/m.0/cv1/conv/Conv: | ██ | 2.761%
/model.5/conv/Conv: | ██ | 2.629%
/model.4/cv2/conv/Conv: | ██ | 2.298%
/model.2/cv1/conv/Conv: | █ | 2.107%
/model.2/m.0/cv2/conv/Conv: | █ | 2.095%
/model.4/m.0/cv1/conv/Conv: | █ | 2.069%
/model.23/cv3.1/cv3.1.2/Conv: | █ | 1.744%
/model.1/conv/Conv: | █ | 1.631%
/model.2/m.0/cv1/conv/Conv: | █ | 1.583%
/model.4/m.0/cv2/conv/Conv: | █ | 1.126%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.535%
/model.0/conv/Conv: | | 0.067%
Analysing Layerwise quantization error:: 100%|██████████| 98/98 [10:49<00:00, 6.63s/it]
Layer | NOISE:SIGNAL POWER RATIO
/model.9/cv2/conv/Conv: | ████████████████████ | 2.976%
/model.2/cv2/conv/Conv: | ███████████ | 1.610%
/model.3/conv/Conv: | ██████ | 0.854%
/model.2/cv1/conv/Conv: | ████ | 0.543%
/model.1/conv/Conv: | ███ | 0.487%
/model.8/cv1/conv/Conv: | ███ | 0.414%
/model.4/cv2/conv/Conv: | ███ | 0.397%
/model.0/conv/Conv: | ██ | 0.364%
/model.6/m.0/cv3/conv/Conv: | ██ | 0.230%
/model.5/conv/Conv: | █ | 0.181%
/model.2/m.0/cv2/conv/Conv: | █ | 0.144%
/model.13/cv2/conv/Conv: | █ | 0.140%
/model.2/m.0/cv1/conv/Conv: | █ | 0.138%
/model.4/cv1/conv/Conv: | █ | 0.129%
/model.16/cv2/conv/Conv: | █ | 0.122%
/model.23/cv4.2/cv4.2.0/conv/Conv: | █ | 0.120%
/model.4/m.0/cv1/conv/Conv: | █ | 0.107%
/model.23/cv4.1/cv4.1.0/conv/Conv: | █ | 0.096%
/model.19/cv2/conv/Conv: | █ | 0.078%
/model.23/cv2.2/cv2.2.2/Conv: | █ | 0.076%
/model.4/m.0/cv2/conv/Conv: | | 0.071%
/model.8/m.0/m/m.1/cv1/conv/Conv: | | 0.071%
/model.6/cv2/conv/Conv: | | 0.067%
/model.6/cv1/conv/Conv: | | 0.066%
/model.17/conv/Conv: | | 0.060%
/model.23/cv4.2/cv4.2.1/conv/Conv: | | 0.057%
/model.22/m.0/m/m.1/cv1/conv/Conv: | | 0.056%
/model.16/cv1/conv/Conv: | | 0.051%
/model.10/cv1/conv/Conv: | | 0.050%
/model.23/cv4.2/cv4.2.2/Conv: | | 0.046%
/model.22/cv2/conv/Conv: | | 0.044%
/model.7/conv/Conv: | | 0.043%
/model.10/m/m.0/attn/pe/conv/Conv: | | 0.043%
/model.10/cv2/conv/Conv: | | 0.037%
/model.19/cv1/conv/Conv: | | 0.037%
/model.8/cv2/conv/Conv: | | 0.036%
/model.13/cv1/conv/Conv: | | 0.036%
/model.6/m.0/m/m.1/cv1/conv/Conv: | | 0.033%
/model.22/m.0/cv3/conv/Conv: | | 0.031%
/model.19/m.0/cv1/conv/Conv: | | 0.027%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | | 0.026%
/model.8/m.0/cv1/conv/Conv: | | 0.025%
/model.19/m.0/cv2/conv/Conv: | | 0.025%
/model.8/m.0/cv3/conv/Conv: | | 0.024%
/model.10/m/m.0/attn/qkv/conv/Conv: | | 0.023%
/model.8/m.0/m/m.0/cv1/conv/Conv: | | 0.023%
/model.22/m.0/cv1/conv/Conv: | | 0.021%
/model.6/m.0/m/m.0/cv1/conv/Conv: | | 0.021%
/model.23/cv2.0/cv2.0.0/conv/Conv: | | 0.020%
/model.6/m.0/cv1/conv/Conv: | | 0.020%
/model.23/cv4.0/cv4.0.0/conv/Conv: | | 0.019%
/model.9/cv1/conv/Conv: | | 0.018%
/model.23/cv4.1/cv4.1.2/Conv: | | 0.018%
/model.23/cv2.1/cv2.1.1/conv/Conv: | | 0.018%
/model.13/m.0/cv1/conv/Conv: | | 0.016%
/model.23/cv2.1/cv2.1.0/conv/Conv: | | 0.016%
/model.23/cv4.1/cv4.1.1/conv/Conv: | | 0.016%
/model.16/m.0/cv2/conv/Conv: | | 0.015%
/model.10/m/m.0/attn/proj/conv/Conv: | | 0.013%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | | 0.013%
/model.8/m.0/m/m.0/cv2/conv/Conv: | | 0.013%
/model.16/m.0/cv1/conv/Conv: | | 0.012%
/model.23/cv2.2/cv2.2.0/conv/Conv: | | 0.011%
/model.20/conv/Conv: | | 0.011%
/model.22/m.0/m/m.0/cv1/conv/Conv: | | 0.011%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | | 0.011%
/model.8/m.0/m/m.1/cv2/conv/Conv: | | 0.010%
/model.23/cv2.0/cv2.0.2/Conv: | | 0.009%
/model.10/m/m.0/attn/MatMul: | | 0.009%
/model.22/cv1/conv/Conv: | | 0.009%
/model.13/m.0/cv2/conv/Conv: | | 0.008%
/model.23/cv2.2/cv2.2.1/conv/Conv: | | 0.008%
/model.23/cv2.1/cv2.1.2/Conv: | | 0.007%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | | 0.007%
/model.22/m.0/m/m.1/cv2/conv/Conv: | | 0.007%
/model.6/m.0/m/m.0/cv2/conv/Conv: | | 0.006%
/model.22/m.0/m/m.0/cv2/conv/Conv: | | 0.006%
/model.23/cv4.0/cv4.0.1/conv/Conv: | | 0.005%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | | 0.005%
/model.23/cv4.0/cv4.0.2/Conv: | | 0.004%
/model.6/m.0/m/m.1/cv2/conv/Conv: | | 0.004%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | | 0.004%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | | 0.003%
/model.23/cv3.2/cv3.2.2/Conv: | | 0.003%
/model.10/m/m.0/attn/MatMul_1: | | 0.002%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | | 0.002%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | | 0.002%
/model.23/cv2.0/cv2.0.1/conv/Conv: | | 0.002%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.2/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | | 0.000%
/model.6/m.0/cv2/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | | 0.000%
/model.8/m.0/cv2/conv/Conv: | | 0.000%
/model.22/m.0/cv2/conv/Conv: | | 0.000%
ESP32-P4 Quantization results
After applying QAT to 8-bit quantization, the quantized model’s Pose mAP50:95 on COCO improves to 44.9% with the same inputs, while cumulative errors of out layers are significantly reduced. Compared to the other two quantization methods, the 8-bit QAT quantized model achieves the highest quantization accuracy with the lowest inference latency.
Note
The mAP results mentioned in this document were computed using Ultralytics version 8.4.50.
Model deployment
Object detection base class
Pre-process
ImagePreprocessor class contains the common pre-precoess pipeline, color conversion, crop, resize, normalization, quantize。