Results are not good after training efficientnetv2 as backbone .

[akashAD98]

I have tried coco2017train data to train this model . its trained for 2 lakhs steps & the loss is in the range of 0.7 to 1 for all 2 lakh steps. & results are worst on validation data .

I have attached all files , please have look & let me know. What's wrong with this model or what mistake i have done during the training model ?

1. the command used for training model

tried faster_rcnn_R50_FPN_weight from detectron2.

python build.py --config-file D:\TFOD_efficientdet\efficient_net_v2\efficient_net_v2\config\effnet_coco.yaml --num_gpus 1 --weights D:\TFOD_efficientdet\efficient_net_v2\efficient_net_v2\weights\faster_rcnn_R50_FPN.pkl --output_dir 'D:\TFOD_efficientdet\efficient_net_v2\efficient_net_v2\output_DIR_base2

Im using AWS machine Tesla v4 for training .

config file config.yaml.txt

log file: logfile.txt

MAP on coco2017 validation data

stances_results.json [06/26 09:25:29 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.58s) creating index... index created! [06/26 09:25:29 d2.evaluation.fast_eval_api]: Evaluate annotation type bbox [06/26 09:25:39 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 9.36 seconds. [06/26 09:25:39 d2.evaluation.fast_eval_api]: Accumulating evaluation results... [06/26 09:25:40 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 1.39 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.042 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.086 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.040 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.068 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.089 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.022 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.121 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.219 [06/26 09:25:40 d2.evaluation.coco_evaluation]: Evaluation results for bbox:

AP	AP50	AP75	APs	APm	APl
4.233	8.554	3.689	1.000	4.044	6.817

[06/26 09:25:40 d2.evaluation.coco_evaluation]: Per-category bbox AP:	category	AP	category	AP	category	AP
person	15.760	bicycle	2.278	car	5.338
motorcycle	5.625	airplane	12.439	bus	12.790
train	10.054	truck	4.306	boat	1.222
traffic light	1.645	fire hydrant	11.190	stop sign	30.312
parking meter	1.661	bench	1.817	bird	0.485
cat	5.196	dog	2.964	horse	6.189
sheep	4.369	cow	7.304	elephant	9.945
bear	13.253	zebra	16.255	giraffe	12.170
backpack	0.097	umbrella	3.546	handbag	0.051
tie	2.221	suitcase	0.408	frisbee	1.692
skis	0.809	snowboard	0.235	sports ball	7.328
kite	3.586	baseball bat	0.000	baseball glove	0.480
skateboard	1.210	surfboard	0.653	tennis racket	1.349
bottle	0.927	wine glass	0.074	cup	1.602
fork	0.000	knife	0.031	spoon	0.000
bowl	3.918	banana	0.074	apple	0.512
sandwich	4.099	orange	4.264	broccoli	0.921
carrot	0.441	hot dog	1.173	pizza	13.876
donut	1.113	cake	1.586	chair	0.631
couch	3.786	potted plant	0.075	bed	6.309
dining table	8.186	toilet	13.220	tv	14.340
laptop	6.258	mouse	0.568	remote	0.000
keyboard	0.885	cell phone	1.573	microwave	4.888
oven	3.542	toaster	0.000	sink	4.386
refrigerator	5.160	book	0.333	clock	8.559
vase	0.852	scissors	0.000	teddy bear	2.242
hair drier	0.000	toothbrush	0.000

[06/26 09:25:41 d2.engine.defaults]: Evaluation results for coco_2017_val in csv format: [06/26 09:25:41 d2.evaluation.testing]: copypaste: Task: bbox [06/26 09:25:41 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl [06/26 09:25:41 d2.evaluation.testing]: copypaste: 4.2330,8.5540,3.6886,1.0001,4.0443,6.8172

This are few results:

Thank you so much, I'm looking forward to your response.

[iKrishneel]

For faster_rcnn_R50_FPN_weight you will need to your ResNet50 backbone. This repo uses efficient_net_v2 as backbone. You can try with this weight

[akashAD98]

thanks . by the way how did you got efficient_net_v2 weight? in the official repo, they haven't published the weight. is there any way to generate these weights using ckpt ?

[iKrishneel]

I trained it from scratch for few epoch.

[akashAD98]

1.can you share which weight you have used for training it from scratch?

2.I trained on the top of your weight i.e (40k steps), your model AP50: 47%, & when I trained my model i got AP50 : 41 % MAP & my batch size is 4 & Even I set very less learning rate. what is the reason behind it?

3.its possible to get the best weight is there any script?

[iKrishneel]

The weight I shared previously should be good initialization for fine-tuning on downstream tasks. You will have to play around with the hyperparameters. If using a custom dataset with fewer samples, maybe it will be better to freeze some backbone layers.

[ammarmuflih]

@iKrishneel do you have mask r-cnn weight?