SSD: Single Shot MultiBox Detector

Pretrained Weights - https://drive.google.com/drive/folders/1_DYYDJUfwLIvGBDnM3hMFNgkVRZW6MgX?usp=sharing

Place the Pretrained Weights inside the path Trained_models/

Accuracy

Dataset	Dtype	Runtime	100 Images	Full Dataset
COCO	FP32	Pytorch	mAPtest - 0.805	mAPtest - 25.048
COCO	FP32	ONNX Runtime	mAPtest - 0.805	mAPtest - 25.048
COCO	FP16	ONNX Runtime	mAPtest- 0.808	mAPtest-25.062
COCO	INT8	ONNX Runtime(Quantize_static)	mAPtest - 0.810	mAPtest - 23.826
COCO	INT8	TVM	mAPtest - 0.811	mAPtest- 23.827

Make sure to put the files as the following structure (The root folder names coco):

coco
├── annotations
│   ├── instances_train2017.json
│   └── instances_val2017.json
│── train2017
└── val2017

Docker

Build:

./devtools/build.sh

Run:

./devtools/run.sh

Pytorch Baseline validation :

Please use the below command to run validation for pytorch model

python -m torch.distributed.launch --nproc_per_node=1  --master_port=25678 train.py --model ssd --batch-size 1 --data-path datasets_coco  -c config/config.yaml --rt val --subset 5000

You will get the results:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.25048
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.42364
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.25828
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.07166
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.27042
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.39630
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.23743
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.34527
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.36238
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.11909
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.39848
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.54719

FP32 ONNX Export:

Please use the below command to export FP32 ONNX model:

python test_dataset.py --pretrained-model trained_models/SSD.pth --data-path datasets_coco -c config/config.yaml --type fp32_model

FP32 ONNX Runtime:

Please use the below command to run inference for FP32 ONNX model:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=25678 train.py --model ssd --batch-size 1 --data-path datasets_coco --rt Onnxruntime -c config/config.yaml --mtype fp32_model --subset 5000

You will get the results:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.25048
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.42364
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.25828
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.07166
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.27042
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.39630
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.23743
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.34527
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.36238
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.11909
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.39848
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.54719

FP16 ONNX Export:

Please use the below command to export FP16 ONNX model:

python test_dataset.py --pretrained-model trained_models/SSD.pth --data-path datasets_coco -c config/config.yaml --type fp16_model

FP16 ONNX runtime:

Please use the below command to run inference for FP16 ONNX model:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=25678 train.py --model ssd --batch-size 1 --data-path datasets_coco --rt Onnxruntime -c config/config.yaml --mtype fp16_model --subset 5000

You will get the results:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.25062
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.42369
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.25866
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.07168
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.27035
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.39675
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.23746
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.34524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.36226
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.11905
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.39844
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.54740

INT8 ONNX Export:

Please use the below command to Convert FP32 ONNX model into Int8 ONNX model:

python test_dataset.py --pretrained-model trained_models/SSD.pth --data-path datasets_coco -c config/config.yaml --type Quant_ONNX_Export --subset 100

INT8 ONNX Runtime:

Please use the below command to run inference for Int8 ONNX model:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=25678 train.py --model ssd --batch-size 1 --data-path datasets_coco --rt Onnxruntime -c config/config.yaml --mtype QDQ_model --subset 5000

You will get the results:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.23826
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.40493
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.24463
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.06377
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.25429
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.38037
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.23168
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.33475
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.35305
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.11472
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.38767
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.53644

TVM CPU Convertion :

Please use the below command to convert Int8 model into TVM format:

 python test_dataset.py --pretrained-model trained_models/SSD.pth --data-path datasets_coco -c config/config.yaml --type Convert_tvm

TVM CPU Evaluation :

Please use the below command to run inference for TVM CPU:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=25678 train.py --model ssd --batch-size 1 --data-path datasets_coco --rt tvm -c config/config.yaml --mtype evaluate_tvm --deploy_cfg config/tvm_cpu.py  --subset 5000

You will get the results:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.24424                                                                                                    
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.41253                                                                                                    
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.25204
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.06570
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.26109
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.38912
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.23609
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.34253
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.36159
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.11826
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.39927
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.54424

Name		Name	Last commit message	Last commit date
Latest commit History 97 Commits
config		config
devtools		devtools
src		src
Dockerfile		Dockerfile
README.md		README.md
sample.py		sample.py
test_dataset.py		test_dataset.py
train.py		train.py

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SSD: Single Shot MultiBox Detector

Pretrained Weights - https://drive.google.com/drive/folders/1_DYYDJUfwLIvGBDnM3hMFNgkVRZW6MgX?usp=sharing

Accuracy

Docker

Pytorch Baseline validation :

Please use the below command to run validation for pytorch model

You will get the results:

FP32 ONNX Export:

Please use the below command to export FP32 ONNX model:

FP32 ONNX Runtime:

Please use the below command to run inference for FP32 ONNX model:

You will get the results:

FP16 ONNX Export:

Please use the below command to export FP16 ONNX model:

FP16 ONNX runtime:

Please use the below command to run inference for FP16 ONNX model:

You will get the results:

INT8 ONNX Export:

Please use the below command to Convert FP32 ONNX model into Int8 ONNX model:

INT8 ONNX Runtime:

Please use the below command to run inference for Int8 ONNX model:

You will get the results:

TVM CPU Convertion :

Please use the below command to convert Int8 model into TVM format:

TVM CPU Evaluation :

Please use the below command to run inference for TVM CPU:

You will get the results:

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SSD: Single Shot MultiBox Detector

Pretrained Weights - https://drive.google.com/drive/folders/1_DYYDJUfwLIvGBDnM3hMFNgkVRZW6MgX?usp=sharing

Accuracy

Docker

Pytorch Baseline validation :

Please use the below command to run validation for pytorch model

You will get the results:

FP32 ONNX Export:

Please use the below command to export FP32 ONNX model:

FP32 ONNX Runtime:

Please use the below command to run inference for FP32 ONNX model:

You will get the results:

FP16 ONNX Export:

Please use the below command to export FP16 ONNX model:

FP16 ONNX runtime:

Please use the below command to run inference for FP16 ONNX model:

You will get the results:

INT8 ONNX Export:

Please use the below command to Convert FP32 ONNX model into Int8 ONNX model:

INT8 ONNX Runtime:

Please use the below command to run inference for Int8 ONNX model:

You will get the results:

TVM CPU Convertion :

Please use the below command to convert Int8 model into TVM format:

TVM CPU Evaluation :

Please use the below command to run inference for TVM CPU:

You will get the results:

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