EfficientNetB0 for classifier(AKA Backbone) and 4 YOLO Layers + BiFPN for detector on darknet Framework.
- enet4y2bifpn with 4.364 to calculate convolution layers
- enet4y2bifpn has Bidirectional Feature Pylamid Network
- 20MB weights file size
- 20days @ V100 GPU for taining with COCO
- 49.5 mAP50 with COCO 5K
- 81.0 mAP50 with VOC2007 test(as additional information)
enet4y2bifpn network structure
The original cfg which on AlexeyAB includes 2 YOLO Layers as detector.
enet4y2bifpn and the original enet-coco have same backbone classifier therefore use same backbone weights.
original README
$ mkdir work;cd work
$ ./scripts/get_coco_dataset.sh
backbone weigts
This weights of classifier may be generated for Cross Stage Partial Networks.
enet4y2bifpn-coco weights
This weights of detector is generated for enet4y2bifpn-coco.cfg.
- write coco.data
classes = 80
train = work/coco/trainvalno5k.txt
valid = work/coco/5k.txt
names = data/coco.names
backup = backup4y2
-
download backbone weights 'enetb0_final.weights' from Website
-
start training
using 24batch size.
$ ./darknet detector train coco.data cfg/enet4y2bifpn-coco.cfg enetb0_final.weights
Unfortunately wait 20days even if V100 GPGPU.
enet4y2bifpn-coco estimation mAP against enet-coco(detector as reference)
Notice:using 64batch size
Summary via pycocotools
| / | AP50-95 | AP50 | AP75 | Small | Medium | Large | Judgement |
|---|---|---|---|---|---|---|---|
| enet-coco | 0.240 | 0.452 | 0.231 | 0.086 | 0.260 | 0.369 | criterion |
| enet4y2-coco | 0.236 | 0.471 | 0.213 | 0.091 | 0.258 | 0.339 | target |
| enet4y2bifpn-coco | 0.255 | 0.495 | 0.241 | 0.099 | 0.279 | 0.363 | target |
| win | win | win | win | win | lose | result |
enet4y2bifpn-coco.cfg pycocotools details
$ ./darknet detector map coco.data cfg/enet4y2bifpn-coco.cfg backup4y2/enet4y2bifpn-coco-bigBatch_final.weights
$ ./scripts/pycocoeval.py -r results/coco_results.json -g work2/coco/annotations/instances_val2014.json
0.25474642780790624
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.255
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.495
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.241
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.099
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.279
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.363
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.229
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.354
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.377
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.409
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515
enet4y2bifpn-voc estimation mAP as additional information
Summary via darknet map
| / | AP50 | Comments |
|---|---|---|
| enet-voc | 0.804 | criterion |
| enet4y2-voc | 0.792 | target |
| enet4y2bifpn-voc | 0.810 | target |
./darknet detector map voc.data cfg/enet4y2bifpn-voc.cfg backup4y2VOC/enet4y2-voc_best.weights
class_id = 0, name = aeroplane, ap = 89.30% (TP = 235, FP = 31)
class_id = 1, name = bicycle, ap = 89.56% (TP = 277, FP = 48)
class_id = 2, name = bird, ap = 80.07% (TP = 350, FP = 81)
class_id = 3, name = boat, ap = 70.80% (TP = 181, FP = 94)
class_id = 4, name = bottle, ap = 68.20% (TP = 314, FP = 159)
class_id = 5, name = bus, ap = 90.40% (TP = 182, FP = 41)
class_id = 6, name = car, ap = 89.76% (TP = 1039, FP = 232)
class_id = 7, name = cat, ap = 89.56% (TP = 302, FP = 48)
class_id = 8, name = chair, ap = 64.18% (TP = 511, FP = 441)
class_id = 9, name = cow, ap = 84.06% (TP = 207, FP = 72)
class_id = 10, name = diningtable, ap = 71.67% (TP = 156, FP = 102)
class_id = 11, name = dog, ap = 85.62% (TP = 398, FP = 111)
class_id = 12, name = horse, ap = 90.50% (TP = 303, FP = 59)
class_id = 13, name = motorbike, ap = 89.71% (TP = 272, FP = 73)
class_id = 14, name = person, ap = 85.57% (TP = 3635, FP = 804)
class_id = 15, name = pottedplant, ap = 52.73% (TP = 252, FP = 178)
class_id = 16, name = sheep, ap = 84.67% (TP = 203, FP = 49)
class_id = 17, name = sofa, ap = 75.92% (TP = 191, FP = 125)
class_id = 18, name = train, ap = 86.33% (TP = 234, FP = 56)
class_id = 19, name = tvmonitor, ap = 81.50% (TP = 249, FP = 74)
for conf_thresh = 0.25, precision = 0.77, recall = 0.79, F1-score = 0.78
for conf_thresh = 0.25, TP = 9491, FP = 2878, FN = 2541, average IoU = 59.83 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision ([email protected]) = 0.810054, or 81.01 %