In the previous article, we have developed and practiced weed-related detection. If you are interested, you can read it by yourself:
“Self-built data set, developed and constructed a weed detection and identification system in farmland scenes based on YOLOv7”
“How far is laser weeding from our actual agricultural life? A field crop weed detection and identification system was developed based on yolov8 based on what we have seen recently”
The main purpose of this article is to develop and construct a weed detection and identification system in field crop scenarios based on the full range of yolov5 models with different parameter levels.
First look at the effect:
Next, let’s take a brief look at the data set:
The content of the instance annotation is as follows:
4 0.33125 0.216 0.283929 0.324 4 0.4 0.597 0.292857 0.414 4 0.755357 0.815 0.289286 0.294 0 0.8625 0.582031 0.196875 0.107813 0 0.8875 0.697656 0.196875 0.082812 0 0.607812 0.971875 0.096875 0.05625 1 0.51875 0.073438 0.090625 0.059375 1 0.439063 0.01875 0.084375 0.034375 3 0.044531 0.410938 0.048438 0.078125 0 0.782031 0.427344 0.060937 0.048438
A full series of yolov5 models have been developed here, with the same default training parameter configuration. Next, let’s take a look at the result details:
【n】
Model file:
# YOLOv5 by Ultralytics, GPL-3.0 license #Parameters nc: 5 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
Result details:
【s】
Model file:
# YOLOv5 by Ultralytics, GPL-3.0 license #Parameters nc: 5 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 #Backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] #Head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
Result details:
【m】
Model file:
# YOLOv5 by Ultralytics, GPL-3.0 license #Parameters nc: 5 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
Result details:
【l】
Model file:
# YOLOv5 by Ultralytics, GPL-3.0 license #Parameters nc: 5 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
Result details:
【x】
Model file:
# YOLOv5 by Ultralytics, GPL-3.0 license #Parameters nc: 5 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
Result details:
Next, we will conduct a comparative analysis of five models with different parameter magnitudes.
【Precision Curve】
The Precision-Recall Curve is a visual tool used to evaluate the accuracy performance of a binary classification model under different thresholds. It helps us understand how the model performs at different thresholds by plotting the relationship between precision and recall at different thresholds.
Precision refers to the ratio of the number of samples that are correctly predicted as positive examples to the number of samples that are predicted to be positive examples. Recall refers to the ratio of the number of samples that are correctly predicted as positive examples to the number of samples that are actually positive examples.
The steps to draw the accuracy curve are as follows:
Convert predicted probabilities into binary class labels using different thresholds. Usually, when the predicted probability is greater than the threshold, the sample is classified as a positive example, otherwise it is classified as a negative example.
For each threshold, the corresponding precision and recall are calculated.
Plot precision and recall at each threshold on the same graph to form a precision curve.
Based on the shape and changing trend of the accuracy curve, an appropriate threshold can be selected to achieve the required performance requirements.
By observing the precision curve, we can determine the best threshold according to our needs to balance precision and recall. Higher precision means fewer false positives, while higher recall means fewer false negatives. Depending on specific business needs and cost trade-offs, appropriate operating points or thresholds can be selected on the curve.
Precision curves are often used together with recall curves to provide a more comprehensive analysis of classifier performance and help evaluate and compare the performance of different models.
【Recall Curve】
Recall Curve is a visualization tool used to evaluate the recall performance of a binary classification model under different thresholds. It helps us understand the performance of the model under different thresholds by plotting the relationship between the recall rate at different thresholds and the corresponding precision rate.
Recall refers to the ratio of the number of samples that are correctly predicted as positive examples to the number of samples that are actually positive examples. Recall rate is also called sensitivity (Sensitivity) or true positive rate (True Positive Rate).
The steps to draw the recall curve are as follows:
Convert predicted probabilities into binary class labels using different thresholds. Usually, when the predicted probability is greater than the threshold, the sample is classified as a positive example, otherwise it is classified as a negative example.
For each threshold, the corresponding recall rate and the corresponding precision rate are calculated.
Plot recall and precision at each threshold on the same graph to form a recall curve.
Based on the shape and changing trend of the recall curve, an appropriate threshold can be selected to achieve the required performance requirements.
By observing the recall curve, we can determine the best threshold according to our needs to balance recall and precision. Higher recall means fewer false negatives, while higher precision means fewer false positives. Depending on specific business needs and cost trade-offs, appropriate operating points or thresholds can be selected on the curve.
Recall curves are often used together with precision curves to provide a more comprehensive analysis of classifier performance and help evaluate and compare the performance of different models.
【F1 value curve】
The F1 value curve is a visual tool used to evaluate the performance of a binary classification model under different thresholds. It helps us understand the overall performance of the model by plotting the relationship between Precision, Recall and F1 score at different thresholds.
The F1 score is the harmonic average of precision and recall, which takes into account both performance indicators. The F1 value curve can help us determine a balance point between different precision and recall rates to choose the best threshold.
The steps to draw the F1 value curve are as follows:
Convert predicted probabilities into binary class labels using different thresholds. Usually, when the predicted probability is greater than the threshold, the sample is classified as a positive example, otherwise it is classified as a negative example.
For each threshold, the corresponding precision, recall and F1 score are calculated.
Plot the precision, recall and F1 score at each threshold on the same graph to form an F1 value curve.
According to the shape and changing trend of the F1 value curve, an appropriate threshold can be selected to achieve the required performance requirements.
F1 value curves are often used together with receiver operating characteristic curves (ROC curves) to help evaluate and compare the performance of different models. They provide a more comprehensive analysis of classifier performance, allowing the selection of appropriate models and threshold settings based on specific application scenarios.
[loss comparison curve]
In actual use, considering speed and accuracy, it is recommended that the S series model be preferred.
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