ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights &amp; Biases logging, PyTorch Hub integration

Jocher, Glenn; Stoken, Alex; Borovec, Jirka; NanoCode,; ChristopherSTAN,; Liu, Changyu; Laughing,; tkianai,; yxNONG,; Hogan, Adam; lorenzomammana,; AlexWang,; Chaurasia, Ayush; Diaconu, Laurentiu; Marć, Paweł; wanghaoyang,; ah, ml; Doug,; Durgesh,; Ingham, Francisco; Frederik,; Guilhen,; Colmagro, Adrien; Hu, Ye; Jacobsolawetz,; Poznanski, Jake; Fang, Jiacong; Kim, Jung-Hoon; Doan, Khiem Ngoc; 于力军, Lijun Yu

doi:10.5281/zenodo.4418161

Cited by 44 publications

(50 citation statements)

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“…YOLOv5 [38] is a one-stage target recognition algorithm proposed by Glenn Jocher in 2020. On the basis of differences in network depth and width, YOLOv5 can be divided into four network model versions: YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x.…”

Section: Yolov5 Network Modulementioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

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Section: Yolov5 Network Modulementioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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“…YOLOv5 was released in May 2020 by Jocher et al [51] from Ultralytics LLC (Los Angeles, CA -USA), a different developer from the previous YOLO versions. Although there were some controversies related to the definition as a new YOLO version, YOLOv5 was accepted by the deep learning community [52].…”

Section: Yolov5mentioning

confidence: 99%

“…Although there were some controversies related to the definition as a new YOLO version, YOLOv5 was accepted by the deep learning community [52]. The main advantage of YOLOv5 relies on the usage of the Python language instead of C. The native framework for YOLOv5 is PyTorch, which allows for faster training [51]. In terms of performance metrics, YOLOv5 allows rapid detection with the same accuracy as YOLOv4 [53].…”

Section: Yolov5mentioning

confidence: 99%

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

et al. 2022

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Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE.

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“…The YOLOv5 P6 15 model for object detection is a single-stage detector that has been pre-trained on the COCO 9 data set.…”

Section: Methodsmentioning

confidence: 99%

Detection of acne by deep learning object detection

Sangha¹,

Rizvi

2021

Preprint

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ImportanceState-of-the art performance is achieved with a deep learning object detection model for acne detection. There is little current research on object detection in dermatology and acne in particular. As such, this work is early in this field and achieves state of the art performance.ObjectiveTrain an object detection model on a publicly available data set of acne photos.Design, Setting, and ParticipantsA deep learning model is trained with cross validation on a data set of facial acne photos.Main Outcomes and MeasuresObject detection models for detecting acne for single-class (acne) and multi-class (four severity levels). We train and evaluate the models using standard metrics such as mean average precision (mAP). Then we manually evaluate the model predictions on the test set, and calculate accuracy in terms of precision, recall, F1, true and false positive and negative detections.ResultsWe achieve state-of-the art mean average precision mAP@0.5 value of 37.97 for the single class acne detection task, and 26.50 for the 4-class acne detection task. Moreover, our manual evaluation shows that the single class detection model performs well on the validation set, achieving true positive 93.59 %, precision 96.45 % and recall 94.73 %.Conclusions and RelevanceWe are able to train a high-accuracy acne detection model using only a small publicly available data set of facial acne. Transfer learning on the pre-trained deep learning model yields good accuracy and high degree of transferability to patient submitted photographs. We also note that the training of standard architecture object detection models has given significantly better accuracy than more intricate and bespoke neural network architectures in the existing research literature.Key PointsQuestionCan deep learning-based acne detection models trained on a small data set of publicly available photos of patients with acne achieve high prediction accuracy?FindingsWe find that it is possible to train a reasonably good object detection model on a small, annotated data set of acne photos using standard deep learning architectures.MeaningDeep learning-based object detection models for acne detection can be a useful decision support tools for dermatologists treating acne patients in a digital clinical practice. It can prove a particularly useful tool for monitoring the time evolution of the acne disease state over prolonged time during follow-ups, as the model predictions give a quantifiable and comparable output for photographs over time. This is particularly helpful in teledermatological consultations, as a prediction model can be integrated in the patient-doctor remote communication.

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ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration

Cited by 44 publications

References 0 publications

Plant Disease Recognition Model Based on Improved YOLOv5

Plant Disease Recognition Model Based on Improved YOLOv5

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

Detection of acne by deep learning object detection

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