This solution can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, mask image analysis, etc.
Open the marking software. Prepare a png or jpg image for annotation. It is recommended that the image has the same aspect ratio.
Setting parameters. classes= 4 : The number of detection classes.
Open data/csresnext50-panet-spp-original-optimal.cfg, search for "classes=", under anchors =, there are 3 places where you need to set the number of categories.
Then search for #filters =(classes + 5)x3 #255, modify the filters= below this string, for example : 4 categories, then filters=27, there are 3 places to be set.
To set the category name, please set the category name in data/label.names. The category name must be exactly the same as the category name when labeling.
The labeled converted into yolo voc xml format.
Prepare a list of training images.
- image_path = "data/train/images": Training image path.
- txt = "data/train.txt": The output training image list.
To prepare the verification image list.
- image_path = "data /val/ images": verification image path.
- txt = "data/val.txt": The output verification image list.
Please remember to execute this ipynb before training. -width 608 -height 608 is the size of the image to be trained (can be modified).
If the image is not in size, it will be scaled. It is recommended that the aspect ratio of the image is the same.
After execution, please copy the
anchors = 62, 99, 79,223, 160,192, 73,469, 424,111, 331,225, 254,369, 151,640, 315,603
Paste this group of numbers on all anchors in data/csresnext50-panet-spp-original-optimal.cfg, there are three places that need to be modified. Next, modify width=608 and height=608 in cfg, which need to be the same as the value set by ipynb.
For training, if the device supports GPU accelerated computing, please select 6_train_GPU, otherwise, please select 6_train_CPU.
model/csresnext50-panet-spp-original-optimal_best.weights ：Continue training with this model. If you do not continue, please delete this parameter.
os.system("start src/darknet/build/darknet/x64/darknet_no_gpu.exe detector train data/voc.data data/csresnext50-panet-spp-original-optimal.cfg
model/csresnext50-panet-spp-original-optimal_best.weights -map -clear")
Inferring a single image.
- model/csresnext50-panet-spp-original-optimal_best.weights : Inferred model.
- data/test/images/inclusion-2.jpg : Inferred image.
- -thresh 0.75 : The threshold of inference.
Use webcam inference. -c 0 is the webcam device id.
Infer all images in the folder.
- image_path = "data/test/images" : Inference folder (jpg, png).
- model_path = "model/csresnext50-panet-spp-original-optimal_best.weights" : Inferred model.
Inference API, run 10_inference_api_CPU.ipynb to open the server, and then run 11_inference_api_browser.ipynb, jump out of the browser, you can select a picture for inference.
- --port 8801 : The opened port.
- --model_file model/csresnext50-panet-spp-original-optimal_best.weights : Inferred model.
- --threshold 0.7 : Inference threshold, the higher the value, the stricter the similarity.
Use the trained model to infer, and automatically mark the voc xml format.
- image_path = "data/auto_labeling/image" :The image folder to be annotated.
- model_path = "model/csresnext50-panet-spp-original-optimal_best.weights": The model to be inferred.
- output_folder = 'data/auto_labeling/annotation': The path of the output annotation file.
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