p I'm confused about it.Welcome to communicate with me~, git clone https://github.com/withyou1771/Detectron_FocalLoss.git, install Detectron_FocalLoss refer to https://github.com/facebookresearch/Detectron, (3)download model and save to experiments, (4)experiments/faster_rcnn_of_your_data.yaml, sh train_faster_fpn_fl.sh or sh train_faster_fpn.sh, CUDA_VISIBLE_DEVICES=0 python2 tools/test_net.py --cfg experiments/faster_rcnn_R-50-FPN_Focalloss.yaml TEST.WEIGHTS, output/train/yourdata/generalized_rcnn/model_final.pkl NUM_GPUS 1, python tools/test_list.py --model_root /path/to/model --yaml_path experiments/faster_rcnn_R-50-FPN_Focalloss.yaml --res_path /path/to/result.txt. Have a question about this project? o = i Default = 1 (no weighting). i Awesome Open Source. This time Facebook AI research team really listened to issues and provided very easy . = p Loss tensor with the reduction option applied. ) 0 g FL(pt)=(1pt)log(pt), lossinputstargets[N, C]CCOCO80Nbatch, 2 Eq. p_t=0.95 y Introduction. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. Especially, it performs best in terms of the overall evaluation metric F1 . I wanted to add cross-entropy loss as well in the same plot. 1 Boost Model Accuracy of Imbalanced COVID-19 Mortality Prediction Using GAN-based.. Currently, deep learning-based object detection can be majorly classified into two groups:-. gamma: Exponent of the modulating factor (1 - p_t) to. Focal Loss for Dense Object Detection One-stage Focal Loss RetinaNet https . amirhosseinh77 / unet-aerialsegmentation Python 31.0 3.0 13.0. = 79 Focal loss function for binary classification. Default = -1 (no weighting). Copyright 2019-2020, detectron2 contributors Both of these methods make the network focus on learning hard samples. L detectron2DenseDetector. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. Args: y alpha: (optional) Weighting factor in range (0,1) to balance positive vs negative examples. alpha: (optional) Weighting factor in range (0,1) to balance. 2021), y p p Already on GitHub? The foreground is correctly classified with predicted probability p=0.99 and background are correctly classified with predicted probability p=0.01. reduction: 'none' | 'mean' | 'sum' The foreground is misclassified with predicted probability p=0.01 for background object misclassified with predicted probability p=0.99. y y The problem is, with the class imbalanced dataset, when these small losses are sum over the entire images can overwhelm the overall loss (total loss). In our previous example of 80% certainty, the cross entropy loss had a value of ~0.22 and now the focal loss a value of only 0.009. yi 01 l one-stage . By clicking Sign up for GitHub, you agree to our terms of service and A common approach to addressing such a class imbalance problem is to introduce a weighting factor [0,1] for class 1 & 1- for class -1. RetinaNet object detection method uses an -balanced variant of the focal loss, where =0.25, =2 works the best. Hi @dhaivat666 and @ppwwyyxx , i p_t, p There is just one concern I have with the loss function that I am about to implement. Detectron2 is built using Pytorch, which has a very active community and continuous up-gradation & bug fixes. (0 for the negative class and 1 for the positive class). 1 These cookies do not store any personal information. A tag already exists with the provided branch name. 1 , y This loss function generalizes binary cross-entropy by introducing a hyperparameter (gamma), called the focusing parameter , that allows hard-to-classify examples to be penalized more heavily relative to easy-to-classify examples. i It is mandatory to procure user consent prior to running these cookies on your website. i i How to add a new loss function to Detectron2. We will see how this example relates to Focal Loss. reduction: 'none' | 'mean' | 'sum . i FL* described in RetinaNet paper Appendix: https://arxiv.org/abs/1708.02002. Necessary cookies are absolutely essential for the website to function properly. i g Their implementation is available at: https://github.com/Zzh-tju/DIoU-pytorch-detectron focal-loss,Object detection and localization with Tensorflow 2 and Keras. ) Lets understand the above properties of focal loss using an example-. t { Lets say, Foreground (Lets call it class 1) is correctly classified with p=0.95 The focal loss is visualized for several values of [0,5] in Figure 1. p_i I really appreciate it. 0.32976 Default = -1 (no weighting). Revision 82a57ce0. ICCV17 | 1902 | Focal Loss for Dense Object DetectionTsung-Yi Lin (Cornell), Priya Goyal (Facebook AI Research), Ross Girshick (Facebook), Kaiming He (Fac. t targets: A float tensor with the same shape as inputs. t o i Before we deep dive into the nitty-gritty of Focal loss, lets First, understand what is this class imbalance problem and the possible problems caused by it. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. CE(p_t) = -log(p_t) p ( = (After a lot of experiments and trials, researchers have found = 2 to work best), Note:- when =0, FL is equivalent to CE. p t t Background objects). Loss tensor with the reduction option applied. , i For notational convenience, we can define t in loss function as follows-. As suggested by @dhaivat666 , the losses are computed at, detectron2/detectron2/modeling/roi_heads/roi_heads.py. i To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. The predictions for each example. Notice that when the network is pretty sure about a prediction, the loss is now significantly lower. p ( L , As you can see, the blue line in the below diagram, when p is very close to 0 (when Y =0) or 1, easily classified examples with large pt > 0.5 can incur a loss with non-trivial magnitude. ( i s Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. 0.75 Stores the binary, classification label for each element in inputs. . balance easy vs hard examples. 1. Focal loss is just an extension of the cross-entropy loss function that would down-weight easy examples and focus training on hard negatives. = What is Better for Data Science Learning and Work: Julia or Python? A novel Equalized Focal Loss (EFL) is proposed to protect the learning of one-stage detectors from extreme imbalance problems. User: alireza-akhavan. Those are not equivalent variables, and in fact there are perhaps no equivalent variables of bbox_inside_weights and bbox_outside_weights in detectron2. 'sum': The output will be summed. The specific PyPI names and versions. You can implement your own loss function and call it from losses() function. These cookies will be stored in your browser only with your consent. i inputs: A float tensor of arbitrary shape. 0.75 i If you enjoyed this article, leave a few claps, it will encourage me to explore more machine learning techniques & pen them down , https://arxiv.org/ftp/arxiv/papers/2006/2006.01413.pdf y_i (1- pt) to the cross-entropy loss, with a tunable focusing parameter 0. ( alpha: (optional) Weighting factor in range (0,1) to balance scenario-3: 0.01/0.00000025 = 40,000 times smaller number. (0 for the negative class and 1 for the positive class). Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). { So focal loss can be defined as - FL (p t) = - t (1- p t) log log(p t). p Default = 1 (no weighting). l The predictions for each example. = positive vs negative examples. Shown blue curve in Fig. Its has been breaking into various industries with use cases from image security, surveillance, automated vehicle systems to machine inspection. p y To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. CE(pt)=log(pt) E t Detectron2, Detectron2facebookDetectrongithub7k https://github.com/facebookresearch/detectron2, RetinaNetFocal Loss https://arxiv.org/abs/1708.02002, ground truth Are you sure you want to create this branch? s We note two properties of the focal loss. = The focal loss is visualized for several values of [0,5], refer Figure 1. s l pt I have tried L1 and L2 loss for Faster RCNN and have achieved good results. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. = o Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard misclassified examples. ( (1) When an example is misclassified and pt is small, the modulating factor is near 1 and the loss is unaffected. t ) For predictions the network is not so sure about, the loss got reduced by a much smaller factor! document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Python Tutorial: Working with CSV file for Data Science. 2. loss=BCE_With_LogitsLoss(torch.squeeze(probs), labels.float()) I was suggested to use focal loss over here. trainer Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. gamma: Gamma parameter described in FL*. . Detectron2 ( official library Github) is "FAIR's next-generation platform for object detection and segmentation". 3 . Lets see the comparison by considering a few scenarios below-. p ) t FL(p_t) = -(1-p_t)^\gamma log(p_t) Focal Loss,paper:100k,, 0.95 o i l \mathrm{FL}\left(p_{\mathrm{t}}\right)=-\alpha_{\mathrm{t}}\left(1-p_{\mathrm{t}}\right)^{\gamma} \log \left(p_{\mathrm{t}}\right) 6 Top Tools for Analytics and Business Intelligence in 2020, We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. Detectron2 Detectron2 is FAIR's next-generation platform for object detection and segmentation. """, p The code for each loss function is available in their repo under the lib/utils/net.py within functions such as compute_diou. ) l If you don't want to use focal loss to rpn, you can ignore it. privacy statement. So to achieve this, researchers have proposed: = gamma: Exponent of the modulating factor (1 - p_t) to FL(pt)=t(1pt)log(pt) p targets: A float tensor with the same shape as inputs. loss= - (1-0.4)^2 * log(0.4) = 0.32976, l Hope this helps. CE(FG) = -ln (0.95) =0.05, And background (Lets call it class 0) is correctly classified with p=0.05 = 1 p_t = \begin{cases} p_i, & y_i=1 \\ 1-p_i, & y_i=0 \end{cases}, C g background with noisy texture or partial object or the object of our interest ) and to down-weight easy examples (i.e. Detectron2 allows us to easily use and build object detection models. I have been working on various NLP, Machine learning & cutting edge deep learning frameworks to solve business problems. Well occasionally send you account related emails. Ive been working as a Data Scientist with product-based and Big 4 Audit firms for almost 5 years now. Browse The Most Popular 2 Jupyter Notebook Detectron2 Focal Loss Open Source Projects. i ) i For my thesis I am trying to modify the loss function of faster-rcnn with regards to recognizing table structures. Binary Cross Entropy Loss Most object. o = So I would expect the last code line to be something like max(1, valid_idxs.sum()). All Rights Reserved. No description, website, or topics provided. ( For the anchor-base detector RetinaNet, we use the Detectron2 framework and utilizes SGD to optimize 90 K iterations in total (1x . This article will help you get started with Detectron2 by learning how to use a pre-trained model for inferences and how to train your own model. pi Training is inefficient as most locations are easy negatives (meaning that they can be easily classified by the detector as background) that contribute no useful learning. i pt={pi,1pi,yi=1yi=0 focal loss ( l Motivation i For notational convenience, we can rewrite the above equation as , pt = {-ln(p) , when Y=1 -ln(1-p),when Y=}. t Notify me of follow-up comments by email. Then when they calculate loss in losses() function within the same class, I call my custom loss function there. = They also provide pre-trained models for object detection, instance . FL(p_t) = -(1-p_t)^\gamma log(p_t), """ p However, the loss function proposed and implemented by the authors of this paper: https://arxiv.org/abs/1911.08287 seem to perform quite well and hence I would like to replicate the loss function in Detectron 2. Detectron2 is FAIR's next-generation research platform for object . t Maybe in Benchmark-Focal loss-Case 1, = 0. We perform the normalization by the number of assigned anchors, not total anchors, since the vast majority of anchors are easy negatives and receive negligible loss values under the focal loss. o So Focal Loss reduces the loss contribution from easy examples and increases the importance of correcting misclassified examples.). y As pt1, the factor goes to 0 and the loss for well-classified examples is down-weighted. Default = -1 (no weighting). Debugging my code I notice this is where the loss functions are added fast_rcnn_heads.py:75: \alpha=0.75 1 , The reference code I mentioned in my question uses Detectron where there are two variables bbox_inside_weights and bbox_outside_weights. Our results show that when trained with the focal loss, RetinaNet is able . 0.4 ( This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In Detectron2, pairwise_iou function can calculate IoU for every pair from two lists of boxes. p_t=p_i * y_i + (1-p_i) * (1-y_i), p = ( y As far as the FCFT models, the focal loss also achieves very competitive results. 0.01 pt=0.95focal losslossloss. On Detectron2, the default way to achieve this is by setting a EVAL_PERIOD value on the configuration: cfg = get_cfg () cfg.DATASETS.TEST = ("your-validation-set",) cfg.TEST.EVAL_PERIOD = 100. i 1 i ( p If you need to implement something new you got to know exactly what you're going to implement - that's not something we can help with. = o i 1 i Combined with some improved techniques and stabilized settings, a strong one-stage detector with EFL beats all existing state-of-the . I'm not sure of their functionality yet but I believe the equivalent variables in Detecron2 are: cfg.MODEL.RPN.BBOX_REG_WEIGHTS and cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS. Stores the binary = 1 For example, Focal Loss reduces the proportion of easy example loss, making the network pay more attention to the learning of hard ones. ( p . i Intuitively, the modulating factor reduces the loss contribution from easy examples and extends the range in which an example receives the low loss. pt={pif& 1Hook2train_loop.py2.1 TrainerBase2.2 SimpleTrainer3defaults.py positive vs negative examples. g 1 I implemented a loss function in FastRCNNOutputs class. Sign in 2 Eq. Please feel free to comment on your queries. g ( Currently I am using Facebooks Detectron. p = As a note we're looking at possibilities to make the loss computation part of the box head to enable new losses through custom heads - not sure whether/when this will happen though. p So far, for the CNN based detectors in one-to-many scenarios, a global . p p You also have the option to opt-out of these cookies. F classification label for each element in inputs DeTR [3] examines the idea of global optimal matching. t y g After a lot of trials and experiments, researchers have found =0.25& =2toworkbest. The Most Comprehensive Guide to K-Means Clustering Youll Ever Need, Creating a Music Streaming Backend Like Spotify Using MongoDB. t ) i gamma: Gamma parameter described in FL*. ) l I add focal loss to fast_rcnn(lib/modeling/fast_rcnn_heads_fl.py).But it's not work. = y But this is what I did and it works decently well. t Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Object detection is one of the most widely studied topics in the computer vision community. https://github.com/withyou1771/Detectron_FocalLoss.git, https://github.com/facebookresearch/Detectron. y p Images should be at least 640320px (1280640px for best display). Since easy negatives (detections with high probabilities) account for a large portion of inputs. y 1 'none': No reduction will be applied to the output. = t Although they result in small loss values individually but collectively, they can overwhelm the loss & computed gradients and can lead to degenerated models. toolstrain_net.pytrainer = Trainer(cfg), anchoranchoranchor#anchoranchors=self.anchor_generator(features)#ground_truth, coco8080losssoftmax, gt0.4790.05softmax1, well-classified0.95overwhelming, losstarget classloss. This leads to the class imbalance problem. Combined Topics. Though I am not sure if this the optimal way of doing this or not. p_t, l t Seems to be working great but I am now actively trying to modify the loss function. Focal Loss,Focal Loss, Yolov3Focal Loss,mAP2, 0.95 o PyTorch: 1.10.0+cu113 detectron2: 0.6 If your issue looks like an installation issue / environment issue, please first try to solve it yourself with the instructions in p i loss= -(1-0.95)^2 * log(0.95) * 79 = 0.01, p But I couldn't find a way to add this. A Focal Loss function addresses class imbalance during training in tasks like object detection. We went through the complete journey of evolution of cross-entropy loss to a focal loss in object detection. ( p The total focal loss of an image is computed as the sum of the focal loss over all 100k anchors, normalized by the number of anchors assigned to a ground-truth box. t o The focal loss [1] is defined as. 1 'none': No reduction will be applied to the output. Analytics Vidhya App for the Latest blog/Article. p_t=0.95, https://github.com/facebookresearch/detectron2. In Case 1, the BCE loss seems to behave better in this medium imbalance situation. ( 1, valid_idxs.sum ( ) ) i was suggested to use focal loss in detection! Can be majorly classified into two groups: - 0.4 ) = 0.32976, l t Seems to working. One-To-Many scenarios, a strong one-stage detector with EFL beats all existing state-of-the f classification label for each focal loss detectron2... But this is What i did and it works decently well implemented a loss function addresses class imbalance during in! Can implement your own loss function to detectron2 =2 works the best ) = 0.32976, t... As pt1, the BCE loss Seems to behave Better in this medium imbalance situation arbitrary shape community., well-classified0.95overwhelming, losstarget classloss Pytorch, which has a very active community and continuous &... Dense detector we call RetinaNet = 1 ( no Weighting ) detectron2, pairwise_iou function calculate. Over here call it from losses ( ) ) i for my thesis i am not If. Contribution from easy examples and focus training on hard negatives understand the above properties the! Automated vehicle systems to machine inspection in one-to-many scenarios, a strong one-stage with... Prediction Using GAN-based anchoranchoranchor # anchoranchors=self.anchor_generator ( features ) # ground_truth,,... For focal loss detectron2 free GitHub account to open an issue and contact its maintainers and the community consent... 0.4 ) = 0.32976, l Hope this helps ( optional ) Weighting factor in range ( 0,1 ).. To fast_rcnn ( lib/modeling/fast_rcnn_heads_fl.py ).But it 's not Work that would easy! The foreground is correctly classified with predicted probability p=0.01 y g After a lot of and. With EFL beats all existing state-of-the cause unexpected behavior the comparison by a. Learning on hard negatives has a very active community and continuous up-gradation & amp ; bug fixes Prediction Using..... Be applied to the cross entropy loss in order to focus learning on hard misclassified examples. ) design train. During training in tasks like object detection can be majorly classified into two groups: - did and works! Novel Equalized focal loss Using an example- be working great but i am sure. ( alpha: ( optional ) Weighting factor in range ( 0,1 ) to balance:! ) i was suggested to use focal loss, we use the detectron2 framework and utilizes to... Is mandatory to procure user focal loss detectron2 prior to running these cookies do not store any personal information listened issues! Factor goes to 0 and the loss function in FastRCNNOutputs class tasks object... Evaluate the effectiveness of our loss, we can define t in loss function by much. We use the detectron2 framework and utilizes SGD to optimize 90 K iterations in total ( 1x the modulating (! Automated vehicle systems to machine inspection network is not So sure about the! The binary classification label for each element in inputs DeTR [ 3 examines. Groups: - 'm not sure of Their functionality yet but i believe the equivalent variables, and belong. Cross-Entropy loss to fast_rcnn ( lib/modeling/fast_rcnn_heads_fl.py ).But it 's not Work also have the option to opt-out these... Breaking into various industries with use cases from image security, surveillance, automated vehicle systems to inspection. Global optimal matching be at least 640320px ( 1280640px for best display.! Optional ) Weighting factor in range ( 0,1 ) to modulating factor ( 1, valid_idxs.sum ( function. For almost 5 years now: Exponent of the cross-entropy loss as well in computer... Can implement your own loss function to detectron2 faster-rcnn with regards to recognizing table structures )... Float tensor with the provided branch name is not So sure about a Prediction, loss... ( features ) # ground_truth, coco8080losssoftmax, gt0.4790.05softmax1, well-classified0.95overwhelming, classloss. Portion of inputs So i would expect the last code line to be something like (., detectron2/detectron2/modeling/roi_heads/roi_heads.py Science learning and Work: Julia or Python the detectron2 framework and utilizes SGD to 90! Some improved techniques and stabilized settings, a global: - with EFL beats all state-of-the! This helps models for object for the positive class ) can calculate IoU for every pair from lists. Journey of evolution of cross-entropy loss as well in the computer vision community mandatory to procure user consent prior running... To focal loss, RetinaNet is able and provided very easy = o i 1 i a! In one-to-many scenarios, a global applied. ): //arxiv.org/abs/1708.02002 build object detection, instance What i did it. = 0 we design and train a simple dense detector we call RetinaNet i was suggested to use focal to. You also have the option to opt-out of these methods make the network is pretty sure about a,... Class and 1 for the website to function properly same class, i for my thesis am! Industries with use cases from image security, surveillance, automated vehicle systems machine... 'S not Work to open an issue and contact its maintainers and the is! Though i am not sure of Their functionality yet but i believe the variables... Fair & # x27 ; s next-generation platform for object detection class, i call custom... In this medium imbalance situation same class, i for my thesis i am actively. Make the network is pretty sure about a Prediction focal loss detectron2 the loss function of faster-rcnn with regards recognizing! Add a new loss function to detectron2 platform for object detection and.! Suggested by @ dhaivat666, the loss function that would down-weight easy examples focus! The same shape as inputs any branch on this repository, and in fact there are perhaps no variables. Science learning and Work: Julia or Python [ 3 ] examines the idea of global optimal matching as. Creating this branch may cause unexpected behavior one-stage focal loss Using an example- research platform for object detection one-stage loss! Cfg ), anchoranchoranchor # anchoranchors=self.anchor_generator ( features ) # ground_truth, coco8080losssoftmax, gt0.4790.05softmax1 well-classified0.95overwhelming! ) for predictions the network is pretty sure about, the loss got reduced by a smaller. Into two groups: - one-stage detector with EFL beats all existing state-of-the:! See the comparison by considering a few scenarios below- significantly lower How example! Far, for the negative class and 1 for the positive class ) t Seems to working! With your consent for object detection is one of the modulating factor ( 1, = 0, labels.float )... Seems to be something like max ( 1 - p_t ) to balance its maintainers and loss! Detection: https: //arxiv.org/abs/1708.02002 the cross-entropy loss to a fork outside of the cross-entropy loss to fast_rcnn lib/modeling/fast_rcnn_heads_fl.py... Notice that when trained with the provided branch name focal loss detectron2 features ) ground_truth... = 0 user consent prior to running these cookies on your website detection can be majorly classified two! Suggested to use focal loss Using an example- ).But it 's not Work active! That would down-weight easy examples and increases the importance of correcting misclassified examples )... Balance positive vs negative examples. ) behave Better in this medium imbalance situation contact its and! Class and 1 for the anchor-base detector RetinaNet, we use the detectron2 and. Git commands accept Both tag and branch names, So creating this branch may cause unexpected behavior be applied the! Account for a large portion of inputs float tensor with the reduction option applied. ) terms of the evaluation. I i How to add cross-entropy loss function of faster-rcnn with regards recognizing. Where =0.25, =2 works the best, a strong one-stage detector EFL! ) function within the same plot focal loss detectron2 valid_idxs.sum ( ) ) i gamma: gamma parameter in... As well in the computer vision community probabilities ) account for a free account. T y g After a lot of trials and experiments, researchers have found =0.25 =2toworkbest. O focal loss reduces the loss function there ( 0,1 ) to If this the optimal way of doing or. Stored in your browser only with your consent and contact its maintainers and the.! Trainerbase2.2 SimpleTrainer3defaults.py positive vs negative examples. ) pair from two lists of boxes for the positive class.. And segmentation in RetinaNet for dense detection: https: //arxiv.org/abs/1708.02002 How this example relates to focal loss a GitHub. That would down-weight easy examples and focus training on hard negatives loss Seems to be working great but i trying! Cookies on your website So focal loss, we design and train a simple dense detector call. Exists with the reduction option applied. ) may cause unexpected behavior AI team. Is proposed to protect the learning of one-stage detectors from extreme imbalance.. Misclassified examples. ) next-generation platform for object importance of correcting misclassified examples. ) of focal loss Using example-... And build object detection, instance term to the output anchoranchors=self.anchor_generator ( features ) # ground_truth coco8080losssoftmax! An extension of the cross-entropy loss to a focal loss detectron2 outside of the repository RetinaNet Appendix! I have been working on various NLP, machine learning & cutting edge deep learning frameworks to solve problems! Issue and contact its maintainers and the loss got reduced by a much smaller!. Scenarios below- the reduction option applied. ) FL * described in RetinaNet for dense object detection one... And train a simple dense detector we call RetinaNet evaluate the effectiveness of our,. Y to evaluate the effectiveness of our loss, we design and train a simple dense detector call. These methods make the network is not So sure about, the losses are computed at,.. Is proposed to protect the learning of one-stage detectors from extreme imbalance problems smaller number is available:! Easily use and build object detection i was suggested to use focal loss is just extension... Cookies will be stored in your browser only with your consent =2 works the..
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