Step 9 – Creating and Editing XSEG Masks (Sped Up) Step 10 – Setting Model Folder (And Inserting Pretrained XSEG Model) Step 11 – Embedding XSEG Masks into Faces Step 12 – Setting Model Folder in MVE Step 13 – Training XSEG from MVE Step 14 – Applying Trained XSEG Masks Step 15 – Importing Trained XSEG Masks to View in MVEMy joy is that after about 10 iterations, my Xseg training was pretty much done (I ran it for 2k just to catch anything I might have missed). 5. 9 XGBoost Best Iteration. )train xseg. X. The full face type XSeg training will trim the masks to the the biggest area possible by full face (that's about half of the forehead although depending on the face angle the coverage might be even bigger and closer to WF, in other cases face might be cut off oat the bottom, in particular chin when mouth is wide open will often get cut off with. Several thermal modes to choose from. 2) Use “extract head” script. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. 0 using XSeg mask training (100. It works perfectly fine when i start Training with Xseg but after a few minutes it stops for a few seconds and then continues but slower. ogt. How to share XSeg Models: 1. I do recommend che. After training starts, memory usage returns to normal (24/32). DeepFaceLab is the leading software for creating deepfakes. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. 1 Dump XGBoost model with feature map using XGBClassifier. How to share SAEHD Models: 1. 0 using XSeg mask training (100. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep fakes,d. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. 1. XSeg is just for masking, that's it, if you applied it to SRC and all masks are fine on SRC faces, you don't touch it anymore, all SRC faces are masked, you then did the same for DST (labeled, trained xseg, applied), now this DST is masked properly, if new DST looks overall similar (same lighting, similar angles) you probably won't need to add. Already segmented faces can. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Does Xseg training affects the regular model training? eg. Post in this thread or create a new thread in this section (Trained Models). However, since some state-of-the-art face segmentation models fail to generate fine-grained masks in some partic-ular shots, the XSeg was introduced in DFL. #1. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. 1) except for some scenes where artefacts disappear. You can use pretrained model for head. 3. Face type ( h / mf / f / wf / head ): Select the face type for XSeg training. It should be able to use GPU for training. It is now time to begin training our deepfake model. bat opened for me, from the XSEG editor to training with SAEHD (I reached 64 it, later I suspended it and continued training my model in quick96), I am with the folder "DeepFaceLab_NVIDIA_up_to_RTX2080Ti ". Video created in DeepFaceLab 2. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. Describe the XSeg model using XSeg model template from rules thread. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. Get XSEG : Definition and Meaning. 4. On training I make sure I enable Mask Training (If I understand this is for the Xseg Masks) Am I missing something with the pretraining? Can you please explain #3 since I'm not sure if I should or shouldn't APPLY to pretrained Xseg before I. Xseg training functions. Just change it back to src Once you get the. It might seem high for CPU, but considering it wont start throttling before getting closer to 100 degrees, it's fine. traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). . PayPal Tip Jar:Lab:MEGA:. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. - Issues · nagadit/DeepFaceLab_Linux. 262K views 1 day ago. Looking for the definition of XSEG? Find out what is the full meaning of XSEG on Abbreviations. Python Version: The one that came with a fresh DFL Download yesterday. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Manually labeling/fixing frames and training the face model takes the bulk of the time. Download RTT V2 224;Same problem here when I try an XSeg train, with my rtx2080Ti (using the rtx2080Ti build released on the 01-04-2021, same issue with end-december builds, work only with the 12-12-2020 build). 0 instead. Src faceset should be xseg'ed and applied. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. k. Read the FAQs and search the forum before posting a new topic. Enter a name of a new model : new Model first run. Consol logs. I could have literally started merging after about 3-4 hours (on a somewhat slower AMD integrated GPU). 建议萌. Phase II: Training. Repeat steps 3-5 until you have no incorrect masks on step 4. tried on studio drivers and gameready ones. Attempting to train XSeg by running 5. 18K subscribers in the SFWdeepfakes community. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. 2) Use “extract head” script. All images are HD and 99% without motion blur, not Xseg. Please mark. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. DFL 2. The software will load all our images files and attempt to run the first iteration of our training. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. It's doing this to figure out where the boundary of the sample masks are on the original image and what collections of pixels are being included and excluded within those boundaries. The Xseg training on src ended up being at worst 5 pixels over. Introduction. . And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. I have now moved DFL to the Boot partition, the behavior remains the same. learned-prd+dst: combines both masks, bigger size of both. 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. Model training is consumed, if prompts OOM. Post in this thread or create a new thread in this section (Trained Models). How to Pretrain Deepfake Models for DeepFaceLab. . Only deleted frames with obstructions or bad XSeg. Timothy B. 000 it) and SAEHD training (only 80. Src faceset is celebrity. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by nebelfuerst. Again, we will use the default settings. npy . I wish there was a detailed XSeg tutorial and explanation video. I solved my 5. . With the first 30. py","path":"models/Model_XSeg/Model. I have a model with quality 192 pretrained with 750. Deepfake native resolution progress. remember that your source videos will have the biggest effect on the outcome!Out of curiosity I saw you're using xseg - did you watch xseg train, and then when you see a spot like those shiny spots begin to form, stop training and go find several frames that are like the one with spots, mask them, rerun xseg and watch to see if the problem goes away, then if it doesn't mask more frames where the shiniest faces. 192 it). Training XSeg is a tiny part of the entire process. Keep shape of source faces. learned-prd*dst: combines both masks, smaller size of both. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 5. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Solution below - use Tensorflow 2. + new decoder produces subpixel clear result. For a 8gb card you can place on. Applying trained XSeg model to aligned/ folder. 000. Training speed. thisdudethe7th Guest. #5726 opened on Sep 9 by damiano63it. You can use pretrained model for head. . Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. Xseg pred is correct as training and shape, but is moved upwards and discovers the beard of the SRC. XSeg) train; Now it’s time to start training our XSeg model. This is fairly expected behavior to make training more robust, unless it is incorrectly masking your faces after it has been trained and applied to merged faces. Where people create machine learning projects. Where people create machine learning projects. Where people create machine learning projects. . Even though that. Then restart training. But I have weak training. npy","path":"facelib/2DFAN. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. In this DeepFaceLab XSeg tutorial I show you how to make better deepfakes and take your composition to the next level! I’ll go over what XSeg is and some important terminology, then we’ll use the generic mask to shortcut the entire process. Then if we look at the second training cycle losses for each batch size :Leave both random warp and flip on the entire time while training face_style_power 0 We'll increase this later You want only the start of training to have styles on (about 10-20k interations then set both to 0), usually face style 10 to morph src to dst, and/or background style 10 to fit the background and dst face border better to the src faceDuring training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. run XSeg) train. 2. In my own tests, I only have to mask 20 - 50 unique frames and the XSeg Training will do the rest of the job for you. DeepFaceLab code and required packages. 1. 27 votes, 16 comments. 运行data_dst mask for XSeg trainer - edit. Pickle is a good way to go: import pickle as pkl #to save it with open ("train. XSeg training GPU unavailable #5214. SAEHD is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020. In this video I explain what they are and how to use them. Where people create machine learning projects. 000. Pretrained models can save you a lot of time. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Again, we will use the default settings. I've downloaded @Groggy4 trained Xseg model and put the content on my model folder. Do you see this issue without 3D parallelism? According to the documentation, train_batch_size is aggregated by the batch size that a single GPU processes in one forward/backward pass (a. All you need to do is pop it in your model folder along with the other model files, use the option to apply the XSEG to the dst set, and as you train you will see the src face learn and adapt to the DST's mask. Which GPU indexes to choose?: Select one or more GPU. Download Celebrity Facesets for DeepFaceLab deepfakes. But before you can stat training you aso have to mask your datasets, both of them, STEP 8 - XSEG MODEL TRAINING, DATASET LABELING AND MASKING: [News Thee snow apretralned Genere WF X5eg model Included wth DF (nternamodel generic xs) fyou dont have time to label aces for your own WF XSeg model or urt needto quickly pely base Wh. com! 'X S Entertainment Group' is one option -- get in to view more @ The. on a 320 resolution it takes upto 13-19 seconds . If it is successful, then the training preview window will open. #1. In the XSeg viewer there is a mask on all faces. Problems Relative to installation of "DeepFaceLab". Tensorflow-gpu 2. Verified Video Creator. py","path":"models/Model_XSeg/Model. Everything is fast. The only available options are the three colors and the two "black and white" displays. It's doing this to figure out where the boundary of the sample masks are on the original image and what collections of pixels are being included and excluded within those boundaries. 5) Train XSeg. DeepFaceLab is an open-source deepfake system created by iperov for face swapping with more than 3,000 forks and 13,000 stars in Github: it provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of deep learning framework or with model implementation required, while remains a flexible and. Differences from SAE: + new encoder produces more stable face and less scale jitter. . XSeg) train issue by. + pixel loss and dssim loss are merged together to achieve both training speed and pixel trueness. bat. I often get collapses if I turn on style power options too soon, or use too high of a value. Today, I train again without changing any setting, but the loss rate for src rised from 0. **I've tryied to run the 6)train SAEHD using my GPU and CPU When running on CPU, even with lower settings and resolutions I get this error** Running trainer. 0 using XSeg mask training (213. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. When the face is clear enough, you don't need. 0146. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. XSeg) data_dst/data_src mask for XSeg trainer - remove. Download Gibi ASMR Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 38,058 / Size: GBDownload Lee Ji-Eun (IU) Faceset - Face: WF / Res: 512 / XSeg: Generic / Qty: 14,256Download Erin Moriarty Faceset - Face: WF / Res: 512 / XSeg: Generic / Qty: 3,157Artificial human — I created my own deepfake—it took two weeks and cost $552 I learned a lot from creating my own deepfake video. xseg) Data_Dst Mask for Xseg Trainer - Edit. XSeg) data_dst/data_src mask for XSeg trainer - remove. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. If it is successful, then the training preview window will open. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. Model training fails. caro_kann; Dec 24, 2021; Replies 6 Views 3K. workspace. prof. added 5. k. Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. The Xseg needs to be edited more or given more labels if I want a perfect mask. npy","contentType":"file"},{"name":"3DFAN. I have to lower the batch_size to 2, to have it even start. 1. Where people create machine learning projects. In a paper published in the Quarterly Journal of Experimental. 3. In my own tests, I only have to mask 20 - 50 unique frames and the XSeg Training will do the rest of the job for you. 3: XSeg Mask Labeling & XSeg Model Training Q1: XSeg is not mandatory because the faces have a default mask. 4 cases both for the SAEHD and Xseg, and with enough and not enough pagefile: SAEHD with Enough Pagefile:The DFL and FaceSwap developers have not been idle, for sure: it’s now possible to use larger input images for training deepfake models (see image below), though this requires more expensive video cards; masking out occlusions (such as hands in front of faces) in deepfakes has been semi-automated by innovations such as XSEG training;. First one-cycle training with batch size 64. Mar 27, 2021 #2 Could be related to the virtual memory if you have small amount of ram or are running dfl on a nearly full drive. 000 it). py","path":"models/Model_XSeg/Model. , train_step_batch_size), the gradient accumulation steps (a. I've been trying to use Xseg for the first time, today, and everything looks "good", but after a little training, I'm going back to the editor to patch/remask some pictures, and I can't see the mask overlay. However, since some state-of-the-art face segmentation models fail to generate fine-grained masks in some partic-ular shots, the XSeg was introduced in DFL. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega) In addition to posting in this thread or. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. Read the FAQs and search the forum before posting a new topic. That just looks like "Random Warp". Tensorflow-gpu. Expected behavior. 0 XSeg Models and Datasets Sharing Thread. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. 2. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. 1 participant. Does model training takes into account applied trained xseg mask ? eg. With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. slow We can't buy new PC, and new cards, after you every new updates ))). Step 5: Training. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). py by just changing the line 669 to. Enjoy it. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. 1. proper. bat I don’t even know if this will apply without training masks. fenris17. Dst face eybrow is visible. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. From the project directory, run 6. Keep shape of source faces. When SAEHD-training a head-model (res 288, batch 6, check full parameters below), I notice there is a huge difference between mentioned iteration time (581 to 590 ms) and the time it really takes (3 seconds per iteration). 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. Actual behavior XSeg trainer looks like this: (This is from the default Elon Musk video by the way) Steps to reproduce I deleted the labels, then labeled again. Leave both random warp and flip on the entire time while training face_style_power 0 We'll increase this later You want only the start of training to have styles on (about 10-20k interations then set both to 0), usually face style 10 to morph src to dst, and/or background style 10 to fit the background and dst face border better to the src faceDuring training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. 3. 5) Train XSeg. Deep convolutional neural networks (DCNNs) have made great progress in recognizing face images under unconstrained environments [1]. But usually just taking it in stride and let the pieces fall where they may is much better for your mental health. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. bat训练遮罩，设置脸型和batch_size，训练个几十上百万，回车结束。 XSeg遮罩训练素材是不区分是src和dst。 2. First one-cycle training with batch size 64. Business, Economics, and Finance. both data_src and data_dst. Xseg editor and overlays. It must work if it does for others, you must be doing something wrong. 0 XSeg Models and Datasets Sharing Thread. Manually labeling/fixing frames and training the face model takes the bulk of the time. It really is a excellent piece of software. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. I have an Issue with Xseg training. Blurs nearby area outside of applied face mask of training samples. The exciting part begins! Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly. DLF installation functions. I'm facing the same problem. Put those GAN files away; you will need them later. 000 iterations, I disable the training and trained the model with the final dst and src 100. Where people create machine learning projects. I was less zealous when it came to dst, because it was longer and I didn't really understand the flow/missed some parts in the guide. It will take about 1-2 hour. THE FILES the model files you still need to download xseg below. XSeg is just for masking, that's it, if you applied it to SRC and all masks are fine on SRC faces, you don't touch it anymore, all SRC faces are masked, you then did the same for DST (labeled, trained xseg, applied), now this DST is masked properly, if new DST looks overall similar (same lighting, similar angles) you probably won't need to add. Step 5. Describe the AMP model using AMP model template from rules thread. Where people create machine learning projects. And then bake them in. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. Normally at gaming temps reach high 85-90, and its confirmed by AMD that the Ryzen 5800H is made that way. Extract source video frame images to workspace/data_src. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. ]. XSeg-dst: uses trained XSeg model to mask using data from destination faces. You can apply Generic XSeg to src faceset. in xseg model the exclusions indeed are learned and fine, the issue new is in training preview, it doesn't show that , i haven't done yet, so now sure if its a preview bug what i have done so far: - re checked frames to see if. Consol logs. 000 iterations many masks look like. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. soklmarle; Jan 29, 2023; Replies 2 Views 597. XSegged with Groggy4 's XSeg model. learned-prd*dst: combines both masks, smaller size of both. There were blowjob XSeg masked faces uploaded by someone before the links were removed by the mods. 这一步工作量巨大，要给每一个关键动作都画上遮罩，作为训练数据，数量大约在几十到几百张不等。. Notes, tests, experience, tools, study and explanations of the source code. 5. Use Fit Training. after that just use the command. Post in this thread or create a new thread in this section (Trained Models) 2. 1. Oct 25, 2020. 2. So we develop a high-efficiency face segmentation tool, XSeg, which allows everyone to customize to suit specific requirements by few-shot learning. Plus, you have to apply the mask after XSeg labeling & training, then go for SAEHD training. I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. Step 2: Faces Extraction. 2. Also it just stopped after 5 hours. #5732 opened on Oct 1 by gauravlokha. Describe the SAEHD model using SAEHD model template from rules thread. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. I'll try. bat. 000 it). The Xseg training on src ended up being at worst 5 pixels over. Xseg Training is a completely different training from Regular training or Pre - Training. Easy Deepfake tutorial for beginners Xseg. Thread starter thisdudethe7th; Start date Mar 27, 2021; T. Part 2 - This part has some less defined photos, but it's. BAT script, open the drawing tool, draw the Mask of the DST. I didn't try it. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. It really is a excellent piece of software. python xgboost continue training on existing model. You can see one of my friend in Princess Leia ;-) I've put same scenes with different. Requires an exact XSeg mask in both src and dst facesets. The fetch. The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Otherwise, if you insist on xseg, you'd mainly have to focus on using low resolutions as well as bare minimum for batch size. 3. It is now time to begin training our deepfake model. xseg) Train. RTT V2 224: 20 million iterations of training. Hi everyone, I'm doing this deepfake, using the head previously for me pre -trained. Otherwise, you can always train xseg in collab and then download the models and apply it to your data srcs and dst then edit them locally and reupload to collabe for SAEHD training. 4. Usually a "Normal" Training takes around 150. What's more important is that the xseg mask is consistent and transitions smoothly across the frames. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. Where people create machine learning projects. 1256. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. even pixel loss can cause it if you turn it on too soon, I only use those. It will take about 1-2 hour. then copy pastE those to your xseg folder for future training. Requesting Any Facial Xseg Data/Models Be Shared Here. Get any video, extract frames as jpg and extract faces as whole face, don't change any names, folders, keep everything in one place, make sure you don't have any long paths or weird symbols in the path names and try it again. Training; Blog; About;Then I'll apply mask, edit material to fix up any learning issues, and I'll continue training without the xseg facepak from then on. pak” archive file for faster loading times 47:40 – Beginning training of our SAEHD model 51:00 – Color transfer. XSeg 蒙版还将帮助模型确定面部尺寸和特征，从而产生更逼真的眼睛和嘴巴运动。虽然默认蒙版可能对较小的面部类型有用，但较大的面部类型（例如全脸和头部）需要自定义 XSeg 蒙版才能获得. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. To conclude, and answer your question, a smaller mini-batch size (not too small) usually leads not only to a smaller number of iterations of a training algorithm, than a large batch size, but also to a higher accuracy overall, i. Link to that. XSeg-prd: uses trained XSeg model to mask using data from source faces. learned-prd+dst: combines both masks, bigger size of both. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. Four iterations are made at the mentioned speed, followed by a pause of. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when.