pytorch-YOLOv4
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Tianxiaomo
I trained a model for 50 epochs but the AP was still 0.002
Introduction
For a personal project I'd like to detect cars, busses, trucks and their license plates. To accomplish this I wanted to train a custom model using YoloV4 with this repository.
I followed the guidance described on this page: https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/master/Use_yolov4_to_train_your_own_data.md
Based on this I've created a train.txt and val.txt file with the following content:
train.txt (255 lines)
20190413_184109.jpg 2391,883,2704,972,0 1824,470,2983,1309,1 2680,460,2955,629,1 1674,451,2019,771,1 2148,445,2299,510,1
20190413_184118.jpg 1906,1742,2190,1817,0 2789,1231,3468,1512,1 1467,1207,2451,1960,3
20190413_184131.jpg 1672,1834,2409,2102,0 804,-28,3589,2588,3 2913,2234,4035,3023,1
...
val.txt (64 lines)
20190413_184125.jpg 2346,1507,2708,1608,0 1402,1020,3017,2084,1 2880,1089,4018,1966,1 1328,1208,1409,1304,1
20190413_184207.jpg 2524,1097,3268,1495,0 324,7,3656,2275,1 2886,3,3758,277,1 4,-2,898,577,1
20190413_184320.jpg 721,2371,1758,2707,0 81,799,2608,3017,1 2561,905,3233,1138,1 3887,837,4043,1042,1 -8,1126,476,2282,1
...
When we look at the first image, it's tagged as follows:
There's 4 cars tagged and 1 license plate.
Preparation
To start the actual training process I first changed some values in cfg.py:
# I changed this to false because I saw that only then the `cfg.pretrained` was being used.
Cfg.use_darknet_cfg = False
# I changed this to 32 because I was running into GPU Memory errors
Cfg.subdivisions = 32
I copied the val.txt file to pytorch-YOLOv4\data\val.txt.
Training
After this I ran the following command:
python train.py --gpu 0 -pretrained C:\git\pytorch-YOLOv4\yolov4.conv.137.pth -classes 4 -train_label_path C:\ml\data\train.txt -dir C:\ml\images
After 50 epochs though, the results and AP where still really low (also there seems to be a logging error, I haven't looked into that):
Epoch 51/300: 38%|▍| 98/255 [00:41<00:39, 4.02im--- Logging error ---
Traceback (most recent call last):
File "C:\Users\******\.conda\envs\******\lib\logging\__init__.py", line 1028, in emit
stream.write(msg + self.terminator)
File "C:\Users\******\.conda\envs\******\lib\encodings\cp1252.py", line 19, in encode
return codecs.charmap_encode(input,self.errors,encoding_table)[0]
UnicodeEncodeError: 'charmap' codec can't encode character '\uff0c' in position 169: character maps to <undefined>
Call stack:
File "train.py", line 626, in <module>
device=device, )
File "train.py", line 411, in train
scheduler.get_lr()[0] * config.batch))
Message: 'Train step_6400: loss : 34547.26171875,loss xy : 42.09270095825195,loss wh : 7.28180456161499,loss obj : 34455.48046875，loss cls : 42.40758514404297,loss l2 : 12746.814453125,lr : 1.6000000000000004e-06'
Arguments: ()
Epoch 51/300: 100%|▉| 254/255 [01:24<00:00, 4.21iin function convert_to_coco_api...
creating index...
index created!
Accumulating evaluation results...
DONE (t=0.12s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002
2020-11-12 16:49:27,147 train.py[line:446] INFO: Created checkpoint directory
2020-11-12 16:49:27,590 train.py[line:451] INFO: Checkpoint 51 saved !
Epoch 51/300: 100%|▉| 254/255 [02:09<00:00, 1.96i
Epoch 52/300: 100%|▉| 254/255 [01:22<00:00, 4.35iin function convert_to_coco_api...
When I tried to use the model on an image the results also came out strange:
Also if we look at the TensorBoard we see the same issues:
I'm not sure what I'm missing, but for some reason it seems the model is not training
More investigation
I also tried with Cfg.use_darknet_cfg = True and Cfg.classes = 4, however when I do this I keep getting the following error:
2020-11-12 17:47:24,605 train.py[line:611] INFO: Using device cuda
convalution havn't activate linear
convalution havn't activate linear
convalution havn't activate linear
2020-11-12 17:47:26,003 train.py[line:327] INFO: Starting training:
Epochs: 300
Batch size: 64
Subdivisions: 32
Learning rate: 0.001
Training size: 255
Validation size: 64
Checkpoints: True
Device: cuda
Images size: 608
Optimizer: adam
Dataset classes: 4
Train label path:C:\*****\train.txt
Pretrained:
Epoch 1/300: 0%| | 0/255 [00:16<?, ?img/s]
Traceback (most recent call last):
File "train.py", line 626, in <module>
device=device, )
File "train.py", line 380, in train
loss, loss_xy, loss_wh, loss_obj, loss_cls, loss_l2 = criterion(bboxes_pred, bboxes)
File "C:\Users\******\.conda\envs\******\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "train.py", line 242, in forward
output = output.view(batchsize, self.n_anchors, n_ch, fsize, fsize)
RuntimeError: shape '[2, 3, 9, 76, 76]' is invalid for input of size 2945760
Is someone tracking these issues?
@ersheng-ai ,@Tianxiaomo , @KelvinCPChiu , could you maybe chip in?
Is someone tracking these issues?
@ersheng-ai ,@Tianxiaomo , @KelvinCPChiu , could you maybe chip in?
If you are using Cfg.use_darknet_cfg = True, you should try this solution https://github.com/Tianxiaomo/pytorch-YOLOv4/issues/138#issuecomment-721116468.
Did you find any solution to this? I also try to train with a custom dataset. I tried both with CFG.use_darknet_cfg = True and False. No matter what I do I get AP = 0.000 in evaluation after one EPOC of 5000 images. And my loss is in the thousands.
i have the same problem,and my initial loss is 200,000，In addition, although Loss has been decreasing, it was still at 12,000 in 140 epoch,The convergence rate is quite slow，And I found that the losses were just focused on the regression losses，So I think there may be some problems with the calculation of loss
hello, did you solve this problem ? i have trained for hundreds epoch, AR and AR are almost zero when evaluate the model. but when i test single image using models.py, refer to https://github.com/Tianxiaomo/pytorch-YOLOv4/issues/413#issuecomment-817089815. could you give me some advice? please thank you
maybe this: In the function : Yolo_loss.build_target(), the label mismach the pred, one is offset value, one is not
I had the same problem.
What work for me, is using Darknet again with:
cfg.use_darknet = True
and loading your last weights using pytorch default method: load_state_dict not the method provided with the class (i think it should be deleted)
so you have to edit the train.py script.
Hi, I have also the same problem with my custom dataset, after training for 300 epochs the AP is zero and when running inference with the obtained model I cannot detect anything even in the train set. Any idea?
I just abandoned this repo all together and used the original darknet yolov4 instead. Got it to work much quicker and better.
I changed the learning rate and batch size, it works~
---Original--- From: @.> Date: Thu, Feb 24, 2022 19:17 PM To: @.>; Cc: @.@.>; Subject: Re: [Tianxiaomo/pytorch-YOLOv4] I trained a model for 50 epochs but the AP was still 0.002 (#323)
Hi, I have also the same problem with my custom dataset, after training for 300 epochs the AP is zero and when running inference with the obtained model I cannot detect anything even in the train set. Any idea?
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@devedse
When using Cfg.use_darknet_cfg = True, the number of filters must be modified to (5+class)*3 in case of Yolov3
When debugging, iou=0 keeps coming out of bbox loss. I think there's an issue in anchor code or between custom data and anchor box size don't match.
Candidate groups are well drawn between gt and anchor, but nothing matches (iou>0) between pred and anchor. There seems to be a problem with the model output.
Hey guys, I think the reason for AP or AR is quite low might be caused by the inappropriate Cfg.burn_in in cfg.py.
- When I use the default Cfg.burn_in=1000 as Tianxiaomo did and train on coins dataset, the AP and AR is extra small. Later I notice the lr curve and find the lr is some kind like e-7 magnitude growing but very slow, which I guess may further leads to the same extremely slow updating of model weights, resulting in small AP and AR but high loss even after hundreds of epochs.
- Then I boldly change Cfg.burn_in=1, set -l 0.001 and epoch=100, it turns out my guess is right (at least it works for me, and i hope it also works for you :D!!!)
NOTES: At first I just run for around 20 epochs, the metrics are still extra small. So I decide try running more epochs to see what will happen (at least it won't hurt, why don't give it a shot hahhhhh), and the result begins to seem reasonable.
