Add High-res FasterRCNN MobileNetV3 and tune Low-res for speed

[datumbox]

• Rename low-res model.
• Tune low-res model for speed (2x faster) by sacrificing 0.02 mAP.
• Add High-res model.

Low Resolution: Faster R-CNN MobileNetV3-Large 320 FPN

Heavily optimized for speed. Good for actual mobile usage.

Weight checkpoint:

PR3253/3rd_training/34945231/model_23.pth

Validate:

python -m torch.distributed.launch --nproc_per_node=2 --use_env train.py\
 --dataset coco --model fasterrcnn_mobilenet_v3_large_320_fpn --test-only --pretrained

Accuracy metrics:

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.228
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.380
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.232
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.026
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.219
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.444
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.215
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.290
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.294
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.295
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570

Speed Benchmark: 0.1679 sec per image on CPU

High Resolution: Faster R-CNN MobileNetV3-Large FPN

Offers good balance between speed and accuracy, significantly faster than the model with the resnet backbone without sacrificing too much accuracy.

Weight checkpoint:

PR3253/high_res/34974236/model_25.pth

Validate:

python -m torch.distributed.launch --nproc_per_node=2 --use_env train.py\
 --dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --test-only --pretrained

Accuracy metrics:

0: IoU metric: bbox
0:  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.328
0:  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.525
0:  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.343
0:  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
0:  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.363
0:  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.502
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.287
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.427
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.445
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499
0:  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.648

Speed Benchmark: 0.8409 sec per image on CPU

[fmassa]

Looks great, thanks!