I'm trying to object detection with yolov3 and yolov4 and i just want to train 1 class= person.
I'm using COCO datasets.
After training how i can tested my weight file? I want to test all "test dataset" not only 1 image.
I have train loss graphic but this is not enough.
I want to calculate accuracy, precision, recall etc.
I found this examples : https://darknet.gong.im/
./darknet detector test cfg/coco.data cfg/yolov3.cfg weights/yolov3.weights batch ./in_images/ ./out_images/ >./results.txt
Can you tell me how i can compare with success metric this two algorithm?
How can i test yolov3 and yolov4 weight files ?
There is map function for testing the model.
./darknet detector map cfg/coco.data cfg/yolov3.cfg weights/yolov3.weights
If you label your test dataset and give the path of it to the 'valid' field inside the data file, you can use map function over your dataset.
Also you can use -map flag while training to see the map results on the graph.
If you use -map flag while training darknet also saves best weight file so you don't need to find the best one.
Related
The labels for YOLO is like [class, x , y, width, height] . Since the dataset is very large, is there any shortcut to generate the labels for YOLO, or we have to hardcode them through measurement?
Method 1: Using Pre-trained YOLOv4 models.
YOLOv4 models were pre-trained on COCO dataset. So, if your object(s) can be found in this list, then, you can use the pre-trained weights to pseudo-label your object(s).
To process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt, use: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
Method 2: Using Other Pre-trained Models. It's the same concept. Use other pre-trained models to detect your object (as long as they have trained their models on your object), then export/convert the labels to YOLO format.
Method 3: Use hand-crafted feature descriptors. Examples are shape detection, color-based detection, etc.
Method 4: Manual labelling. If everything else fails, do the labelling yourself or hire some data labelling services. Here's a list of tools that you can use if you want to label them yourself.
I'm a beginner with some programming experince. I'm trying to train darkflow with my own dataset. I'm following these instructions.
https://github.com/thtrieu/darkflow
So far I have done the following steps.
installed darkflow and the relevant modules
created test images and made annotations (Pascal VOC).
https://ibb.co/y4HmtGz
https://ibb.co/GkxLshK
If I have understood correctly the darkflow training requires Pascal VOC?
My problem is that I don't know how to start the training. How can I start the training process and how can I test if the neuralnet is working? Am I supposed to get weights as a result of training?
You can choose to use pre-trained weights from here. Download cfg and weights.
Assuming you have darkflow installed, you can train your network like this:
flow --model cfg/<your-config-filename>.cfg --load bin/<filename>.weights --train --annotation train/Annotations --dataset train/Images --epoch 100 --gpu 1.0
If you want to train your network from scratch w/o using any pre-trained weights,
you can do this:
flow --model cfg/<your-config-filename>.cfg --train --annotation train/Annotations --dataset train/Images --epoch 100 --gpu 1.0
After the training starts, model checkpoints are saved inside ckpt directory. You can load latest checkpoint and test on sample images.
I trained my custom object detection with darknet yolov3 untill the average loss decreased down to 0.06 but now i want to train it with more training and test images (maybe also deleting some of the image files). Can I do these steps and continue to training with final .weights file or I should start it from the beginning?
Yes, you can use the currently trained model (.weights file) as the pre-trained model for the new training session. For example, if you use AlexeyAB repository you can train your model by a command like this:
darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74
where darknet53.conv.74 is the pre-trained model.
In the new training session, you can add or remove images. However, the basic configurations should be correct (like the number of classes, etc).
According to the page I mentioned:
in the original repository original repository the
weights-file is saved only once every 10 000 iterations
If you have just modified the data set, but are not interested in changing the model architecture,you can directly resume from the previously saved model using DarkNet in AlexeyAB/darknet. For example,
darknet.exe detector train cfg/obj.data cfg/yolov3.cfg yolov3_weights_last.weights -clear -map
The clear flag will reset iterations saved in the weights, which is appropriate in case of data set changes. That is because the learning rate often depends on the iterations, and you probably don't want to change the configurations.
You need to specify more epochs if you resume. For example if you train to 300/300 then resume will also train to 300 also (starting at 300) unless you specify more epochs..
python train.py --resume
you can resume your training from the previously saved weights, of your custom model.
use the "yolov3_custom_last.weights" instead of the pre-trained default weights.
Incase you find some issues with resuming, try changing the batch size .
this should work and resume your model training with new set of images :)
open the .cfg, find the max_batches code may be in 22 row, set the bigger value:
max_batches = 500200
max_batches is the same to the tranning iteration.
How to continute training after 50000 iteration? #2633
I have created text classification model(.pb) using tensorflow. Prediction is good.
Is it possible to check the sentence using for prediction is already used to train the model or not. I need to retrain the model when new sentence is given to model to predict.
I did some research and couldn't find a way to get the train data only with the pb file because that file only stores the features and not the actual train data(obviously),but if you have the dataset,then you can easily verify duh....
I don't think you can ever find the exact train data with only the trained model,cause the model only contains the features and not the actual train data
The dataset contains images of different sizes.
The pretrained weights are trained on 300x300 resolution.
I am training on widerface dataset where objects are as small as 15x15.
Q1. I want to train with 800x800 resolution do i need to resize all the images manually or this will be done by Tensorflow automatically ?
I am using the following command to train:
python3 /opt/github/models/research/object_detection/legacy/train.py --logtostderr --train_dir=/opt/github/object_detection_retraining/wider_face_checkpoint/ --pipeline_config_path=/opt/github/object_detection_retraining/models/ssd_inception_v2_coco_2018_01_28/pipeline.config
Q2. I also tried training it using the model_main.py but after 1000 iterations it is evaluating the dataset with each iteration.
I am using the following command to train:
python3 /opt/github/models/research/object_detection/model_main.py --num_train_steps=200000 --logtostderr --model_dir=/opt/github/object_detection_retraining/wider_face_checkpoint/ --pipeline_config_path=/opt/github/object_detection_retraining/models/ssd_inception_v2_coco_2018_01_28/pipeline.config
Q3. Also if you can suggest any model i should use for real time face detection apart from mobilenet and inception, please suggest.
Thanks.
Q1. No you do not need to resize manually. See this detailed answer.
Q2. By 1000 iterations you meant steps right? (An iteration counts as a complete cycle of the dataset.) Usually the model performed evaluation after a certain amount of time, e.g. 10 minutes. So in every 10 minutes, the checkpoints are saved and an evaluation of the model on evaluation set is performed.
Q3. SSD models with mobilenet is one of the fast detectors, apart from that you can try YOLO models for real time detection