Extract source video frame images to workspace/data_src. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 2. bat. The dice, volumetric overlap error, relative volume difference. I'm not sure if you can turn off random warping for XSeg training and frankly I don't thing you should, it helps to make the mask training be able to generalize on new data sets. . {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. 0 XSeg Models and Datasets Sharing Thread. I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. Business, Economics, and Finance. Deepfake native resolution progress. X. XSeg in general can require large amounts of virtual memory. Choose the same as your deepfake model. It haven't break 10k iterations yet, but the objects are already masked out. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. After the draw is completed, use 5. Post processing. 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. Blurs nearby area outside of applied face mask of training samples. XSeg) data_src trained mask - apply. Complete the 4-day Level 1 Basic CPTED Course. 000 it), SAEHD pre-training (1. bat’. 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. THE FILES the model files you still need to download xseg below. Where people create machine learning projects. 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. Everything is fast. I've posted the result in a video. 2. Src faceset is celebrity. SRC Simpleware. Keep shape of source faces. then copy pastE those to your xseg folder for future training. 000. Definitely one of the harder parts. From the project directory, run 6. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. GPU: Geforce 3080 10GB. Deep convolutional neural networks (DCNNs) have made great progress in recognizing face images under unconstrained environments [1]. 5. The software will load all our images files and attempt to run the first iteration of our training. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. soklmarle; Jan 29, 2023; Replies 2 Views 597. #4. Xseg training functions. Running trainer. 运行data_dst mask for XSeg trainer - edit. (or increase) denoise_dst. 3. It is now time to begin training our deepfake model. 05 and 0. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep fakes,d. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. . py","contentType":"file"},{"name. 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. 000 it) and SAEHD training (only 80. dump ( [train_x, train_y], f) #to load it with open ("train. 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. added 5. Describe the SAEHD model using SAEHD model template from rules thread. XSeg-prd: uses. load (f) If your dataset is huge, I would recommend check out hdf5 as @Lukasz Tracewski mentioned. Just let XSeg run a little longer. even pixel loss can cause it if you turn it on too soon, I only use those. Training speed. Model training is consumed, if prompts OOM. How to share SAEHD Models: 1. 000 iterations, but the more you train it the better it gets EDIT: You can also pause the training and start it again, I don't know why people usually do it for multiple days straight, maybe it is to save time, but I'm not surenew DeepFaceLab build has been released. On conversion, the settings listed in that post work best for me, but it always helps to fiddle around. workspace. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Actually you can use different SAEHD and XSeg models but it has to be done correctly and one has to keep in mind few things. pkl", "w") as f: pkl. Doing a rough project, I’ve run generic XSeg, going through the frames in edit on the destination, several frames have picked up the background as part of the face, may be a silly question, but if I manually add the mask boundary in edit view do I have to do anything else to apply the new mask area or will that not work, it. . Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. Step 1: Frame Extraction. Where people create machine learning projects. Where people create machine learning projects. v4 (1,241,416 Iterations). ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. bat’. 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. caro_kann; Dec 24, 2021; Replies 6 Views 3K. If it is successful, then the training preview window will open. - Issues · nagadit/DeepFaceLab_Linux. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Hello, after this new updates, DFL is only worst. bat compiles all the xseg faces you’ve masked. After training starts, memory usage returns to normal (24/32). 4. 0 using XSeg mask training (100. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. npy","path. Verified Video Creator. cpu_count = multiprocessing. 0 using XSeg mask training (213. Yes, but a different partition. . Step 5: Training. 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. Where people create machine learning projects. Describe the XSeg model using XSeg model template from rules thread. The result is the background near the face is smoothed and less noticeable on swapped face. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. If your facial is 900 frames and you have a good generic xseg model (trained with 5k to 10k segmented faces, with everything, facials included but not only) then you don't need to segment 900 faces : just apply your generic mask, go the facial section of your video, segment 15 to 80 frames where your generic mask did a poor job, then retrain. The Xseg needs to be edited more or given more labels if I want a perfect mask. Just change it back to src Once you get the. Video created in DeepFaceLab 2. 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. In addition to posting in this thread or the general forum. If it is successful, then the training preview window will open. 3. This forum is for reporting errors with the Extraction process. Container for all video, image, and model files used in the deepfake project. SRC Simpleware. 3. #1. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. . added 5. If your model is collapsed, you can only revert to a backup. Tensorflow-gpu 2. The images in question are the bottom right and the image two above that. Usually a "Normal" Training takes around 150. 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). Without manually editing masks of a bunch of pics, but just adding downloaded masked pics to the dst aligned folder for xseg training, I'm wondering how DFL learns to. DeepFaceLab 2. I have an Issue with Xseg training. DeepFaceLab is the leading software for creating deepfakes. Expected behavior. XSeg training GPU unavailable #5214. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). For a 8gb card you can place on. Training. 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. 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. 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. Make a GAN folder: MODEL/GAN. Post in this thread or create a new thread in this section (Trained Models). Contribute to idorg/DeepFaceLab by creating an account on DagsHub. Lee - Dec 16, 2019 12:50 pm UTCForum rules. I actually got a pretty good result after about 5 attempts (all in the same training session). Video created in DeepFaceLab 2. Step 2: Faces Extraction. 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. py","contentType":"file"},{"name. However, I noticed in many frames it was just straight up not replacing any of the frames. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. Describe the XSeg model using XSeg model template from rules thread. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. It should be able to use GPU for training. 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. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. 1 Dump XGBoost model with feature map using XGBClassifier. 1. DFL 2. com XSEG Stands For : X S Entertainment GroupObtain the confidence needed to safely operate your Niton handheld XRF or LIBS analyzer. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). That just looks like "Random Warp". Phase II: Training. 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 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. But doing so means redo extraction while the XSEG masks just save them with XSEG_fetch, redo the Xseg training, apply, check and launch the SAEHD training. Step 4: Training. BAT script, open the drawing tool, draw the Mask of the DST. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 3X to 4. Training XSeg is a tiny part of the entire process. The training preview shows the hole clearly and I run on a loss of ~. XSeg) data_src trained mask - apply the CMD returns this to me. Xseg apply/remove functions. The software will load all our images files and attempt to run the first iteration of our training. Already segmented faces can. It is used at 2 places. traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. XSeg in general can require large amounts of virtual memory. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. Download Celebrity Facesets for DeepFaceLab deepfakes. DFL 2. You can see one of my friend in Princess Leia ;-) I've put same scenes with different. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. 5) Train XSeg. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. If you want to get tips, or better understand the Extract process, then. Oct 25, 2020. The only available options are the three colors and the two "black and white" displays. Step 5: Merging. Also it just stopped after 5 hours. XSeg Model Training. Then I apply the masks, to both src and dst. Please read the general rules for Trained Models in case you are not sure where to post requests or are looking for. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. #1. Xseg Training is for training masks over Src or Dst faces ( Telling DFL what is the correct area of the face to include or exclude ). Share. XSeg won't train with GTX1060 6GB. I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. The Xseg training on src ended up being at worst 5 pixels over. However in order to get the face proportions correct, and a better likeness, the mask needs to be fit to the actual faces. then i reccomend you start by doing some manuel xseg. 0 using XSeg mask training (213. This one is only at 3k iterations but the same problem presents itself even at like 80k and I can't seem to figure out what is causing it. Enjoy it. == Model name: XSeg ==== Current iteration: 213522 ==== face_type: wf ==== p. XSeg) data_dst mask - edit. 训练Xseg模型. Actual behavior. 000 iterations, I disable the training and trained the model with the final dst and src 100. bat I don’t even know if this will apply without training masks. GPU: Geforce 3080 10GB. Requesting Any Facial Xseg Data/Models Be Shared Here. When the face is clear enough, you don't need. 3. py","contentType":"file"},{"name. bat. learned-prd*dst: combines both masks, smaller size of both. I didn't try it. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I wish there was a detailed XSeg tutorial and explanation video. Post in this thread or create a new thread in this section (Trained Models). DST and SRC face functions. I often get collapses if I turn on style power options too soon, or use too high of a value. 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. . 0rc3 Driver. 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. Sometimes, I still have to manually mask a good 50 or more faces, depending on. . So we develop a high-efficiency face segmentation tool, XSeg, which allows everyone to customize to suit specific requirements by few-shot learning. bat train the model Check the faces of 'XSeg dst faces' preview. 2. Put those GAN files away; you will need them later. 18K subscribers in the SFWdeepfakes community. With the first 30. Where people create machine learning projects. Consol logs. Increased page file to 60 gigs, and it started. 192 it). Describe the XSeg model using XSeg model template from rules thread. 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. . 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). Use XSeg for masking. It really is a excellent piece of software. if i lower the resolution of the aligned src , the training iterations go faster , but it will STILL take extra time on every 4th iteration. It depends on the shape, colour and size of the glasses frame, I guess. Tensorflow-gpu. Run 6) train SAEHD. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. Enter a name of a new model : new Model first run. Where people create machine learning projects. 5. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. XSegged with Groggy4 's XSeg model. XSeg allows everyone to train their model for the segmentation of a spe- Pretrained XSEG is a model for masking the generated face, very helpful to automatically and intelligently mask away obstructions. Read the FAQs and search the forum before posting a new topic. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. Easy Deepfake tutorial for beginners Xseg. The Xseg needs to be edited more or given more labels if I want a perfect mask. 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 ". In addition to posting in this thread or the general forum. Plus, you have to apply the mask after XSeg labeling & training, then go for SAEHD training. 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. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. 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. S. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. I'm facing the same problem. Apr 11, 2022. The guide literally has explanation on when, why and how to use every option, read it again, maybe you missed the training part of the guide that contains detailed explanation of each option. Pretrained XSEG is a model for masking the generated face, very helpful to automatically and intelligently mask away obstructions. How to share XSeg Models: 1. . SAEHD Training Failure · Issue #55 · chervonij/DFL-Colab · GitHub. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. bat，会跳出界面绘制dst遮罩，就是框框抠抠，这是个细活儿，挺累的。 运行train. learned-prd*dst: combines both masks, smaller size of both. Looking for the definition of XSEG? Find out what is the full meaning of XSEG on Abbreviations. 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. Several thermal modes to choose from. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. And then bake them in. Train the fake with SAEHD and whole_face type. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. python xgboost continue training on existing model. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. XSeg in general can require large amounts of virtual memory. Repeat steps 3-5 until you have no incorrect masks on step 4. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. a. 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. xseg) Train. All images are HD and 99% without motion blur, not Xseg. . Model training fails. Where people create machine learning projects. Post_date. 000 it). It will take about 1-2 hour. 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;. Even though that. 1. Mark your own mask only for 30-50 faces of dst video. 5. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. Please mark. 000 it) and SAEHD training (only 80. XSeg question. 00:00 Start00:21 What is pretraining?00:50 Why use i. Notes, tests, experience, tools, study and explanations of the source code. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. The fetch. 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. e, a neural network that performs better, in the same amount of training time, or less. thisdudethe7th Guest. after that just use the command. Copy link 1over137 commented Dec 24, 2020. 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. Sep 15, 2022. 1. 9794 and 0. 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. bat after generating masks using the default generic XSeg model. 3. In addition to posting in this thread or the general forum. It is normal until yesterday. xseg train not working #5389. 3. Run: 5. It learns this to be able to. In this video I explain what they are and how to use them. also make sure not to create a faceset. xseg) Data_Dst Mask for Xseg Trainer - Edit. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. Where people create machine learning projects. MikeChan said: Dear all, I'm using DFL-colab 2. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. 1256. I have an Issue with Xseg training. Grab 10-20 alignments from each dst/src you have, while ensuring they vary and try not to go higher than ~150 at first. DFL 2. It must work if it does for others, you must be doing something wrong. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. 0146. 2) extract images from video data_src. Already segmented faces can. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. XSeg) train. 5) Train XSeg. py","path":"models/Model_XSeg/Model. At last after a lot of training, you can merge. xseg) Data_Dst Mask for Xseg Trainer - Edit. For DST just include the part of the face you want to replace. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. Sometimes, I still have to manually mask a good 50 or more faces, depending on material. 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. 1) except for some scenes where artefacts disappear. DF Admirer. Choose one or several GPU idxs (separated by comma). XSeg) data_dst/data_src mask for XSeg trainer - remove. cpu_count() // 2. First one-cycle training with batch size 64. ]. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by. With a batch size 512, the training is nearly 4x faster compared to the batch size 64! Moreover, even though the batch size 512 took fewer steps, in the end it has better training loss and slightly worse validation loss. How to share AMP Models: 1. 2) Use “extract head” script. With a batch size 512, the training is nearly 4x faster compared to the batch size 64! Moreover, even though the batch size 512 took fewer steps, in the end it has better training loss and slightly worse validation loss. learned-dst: uses masks learned during training. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. ** Steps to reproduce **i tried to clean install windows , and follow all tips . I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. Its a method of randomly warping the image as it trains so it is better at generalization. 4. . py","path":"models/Model_XSeg/Model. 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. Training XSeg is a tiny part of the entire process.