So we develop a high-efficiency face segmentation tool, XSeg, which allows everyone to customize to suit specific requirements by few-shot learning. learned-prd+dst: combines both masks, bigger size of both. 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. 建议萌. Differences from SAE: + new encoder produces more stable face and less scale jitter. Just let XSeg run a little longer. . XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. Manually mask these with XSeg. 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. What's more important is that the xseg mask is consistent and transitions smoothly across the frames. If it is successful, then the training preview window will open. Step 2: Faces Extraction. ]. 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. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. Where people create machine learning projects. 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. Consol logs. 训练Xseg模型. 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. 0rc3 Driver. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. Apr 11, 2022. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. A skill in programs such as AfterEffects or Davinci Resolve is also desirable. Do not mix different age. 6) Apply trained XSeg mask for src and dst headsets. Video created in DeepFaceLab 2. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. Instead of the trainer continuing after loading samples, it sits idle doing nothing infinitely like this:With XSeg training for example the temps stabilize at 70 for CPU and 62 for GPU. [Tooltip: Half / mid face / full face / whole face / head. 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. 1. It should be able to use GPU for training. Deep convolutional neural networks (DCNNs) have made great progress in recognizing face images under unconstrained environments [1]. 1. DST and SRC face functions. DeepFaceLab code and required packages. 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. 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. Double-click the file labeled ‘6) train Quick96. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. Training speed. Increased page file to 60 gigs, and it started. Setting Value Notes; iterations: 100000: Or until previews are sharp with eyes and teeth details. However, when I'm merging, around 40 % of the frames "do not have a face". The dice, volumetric overlap error, relative volume difference. And the 2nd column and 5th column of preview photo change from clear face to yellow. If I train src xseg and dst xseg separately, vs training a single xseg model for both src and dst? Does this impact the quality in any way? 2. Sometimes, I still have to manually mask a good 50 or more faces, depending on material. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. Quick96 seems to be something you want to use if you're just trying to do a quick and dirty job for a proof of concept or if it's not important that the quality is top notch. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I guess you'd need enough source without glasses for them to disappear. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. 5. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Requesting Any Facial Xseg Data/Models Be Shared Here. Applying trained XSeg model to aligned/ folder. Post in this thread or create a new thread in this section (Trained Models). Link to that. 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. In a paper published in the Quarterly Journal of Experimental. 1. As I understand it, if you had a super-trained model (they say its 400-500 thousand iterations) for all face positions, then you wouldn’t have to start training every time. Where people create machine learning projects. Verified Video Creator. Today, I train again without changing any setting, but the loss rate for src rised from 0. 4. GPU: Geforce 3080 10GB. The images in question are the bottom right and the image two above that. first aply xseg to the model. 0 How to make XGBoost model to learn its mistakes. Verified Video Creator. Only deleted frames with obstructions or bad XSeg. Get XSEG : Definition and Meaning. 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. learned-prd+dst: combines both masks, bigger size of both. HEAD masks are not ideal since they cover hair, neck, ears (depending on how you mask it but in most cases with short haired males faces you do hair and ears) which aren't fully covered by WF and not at all by FF,. Where people create machine learning projects. , gradient_accumulation_ste. Video created in DeepFaceLab 2. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). Describe the XSeg model using XSeg model template from rules thread. Already segmented faces can. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. I have to lower the batch_size to 2, to have it even start. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. 0 XSeg Models and Datasets Sharing Thread. 16 XGBoost produce prediction result and probability. ago. 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. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. . Which GPU indexes to choose?: Select one or more GPU. Tensorflow-gpu. 000 iterations, I disable the training and trained the model with the final dst and src 100. Extract source video frame images to workspace/data_src. 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. bat compiles all the xseg faces you’ve masked. It might seem high for CPU, but considering it wont start throttling before getting closer to 100 degrees, it's fine. 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. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. 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;. py by just changing the line 669 to. com! 'X S Entertainment Group' is one option -- get in to view more @ The. Step 5. (or increase) denoise_dst. 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. - Issues · nagadit/DeepFaceLab_Linux. py","path":"models/Model_XSeg/Model. THE FILES the model files you still need to download xseg below. 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. 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. 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. XSeg) data_src trained mask - apply. Where people create machine learning projects. 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. Video created in DeepFaceLab 2. You should spend time studying the workflow and growing your skills. Src faceset should be xseg'ed and applied. py","contentType":"file"},{"name. Several thermal modes to choose from. Final model config:===== Model Summary ==. 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. SAEHD Training Failure · Issue #55 · chervonij/DFL-Colab · GitHub. Sydney Sweeney, HD, 18k images, 512x512. Sometimes, I still have to manually mask a good 50 or more faces, depending on. I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. 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. 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. Sep 15, 2022. XSeg) train issue by. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. Read all instructions before training. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). I've downloaded @Groggy4 trained Xseg model and put the content on my model folder. GPU: Geforce 3080 10GB. XSeg) train; Now it’s time to start training our XSeg model. 000. pak file untill you did all the manuel xseg you wanted to do. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. Part 1. pkl", "r") as f: train_x, train_y = pkl. 0 using XSeg mask training (213. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. Again, we will use the default settings. 0 XSeg Models and Datasets Sharing Thread. I wish there was a detailed XSeg tutorial and explanation video. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. In addition to posting in this thread or the general forum. 2) Use “extract head” script. 522 it) and SAEHD training (534. And for SRC, what part is used as face for training. 00:00 Start00:21 What is pretraining?00:50 Why use i. 2. 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. . Remove filters by clicking the text underneath the dropdowns. when the rightmost preview column becomes sharper stop training and run a convert. Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. bat’. learned-dst: uses masks learned during training. Python Version: The one that came with a fresh DFL Download yesterday. Extra trained by Rumateus. Where people create machine learning projects. Where people create machine learning projects. Download this and put it into the model folder. It will take about 1-2 hour. Step 5: Merging. 000 iterations many masks look like. Where people create machine learning projects. In the XSeg viewer there is a mask on all faces. But usually just taking it in stride and let the pieces fall where they may is much better for your mental health. Copy link. It learns this to be able to. Notes, tests, experience, tools, study and explanations of the source code. 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. 5) Train XSeg. run XSeg) train. python xgboost continue training on existing model. PayPal Tip Jar:Lab:MEGA:. Actual behavior. Plus, you have to apply the mask after XSeg labeling & training, then go for SAEHD training. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. The Xseg needs to be edited more or given more labels if I want a perfect mask. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. I have now moved DFL to the Boot partition, the behavior remains the same. py","path":"models/Model_XSeg/Model. 023 at 170k iterations, but when I go to the editor and look at the mask, none of those faces have a hole where I have placed a exclusion polygon around. #5727 opened on Sep 19 by WagnerFighter. e, a neural network that performs better, in the same amount of training time, or less. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by. 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. Train XSeg on these masks. Enter a name of a new model : new Model first run. 5. DF Vagrant. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. It is now time to begin training our deepfake model. Expected behavior. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. . Model first run. Where people create machine learning projects. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. 9 XGBoost Best Iteration. . Oct 25, 2020. 192 it). XSeg question. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Xseg遮罩模型的使用可以分为训练和使用两部分部分. From the project directory, run 6. 5. It really is a excellent piece of software. Double-click the file labeled ‘6) train Quick96. 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) In addition to posting in this thread or. Container for all video, image, and model files used in the deepfake project. 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. On conversion, the settings listed in that post work best for me, but it always helps to fiddle around. Frame extraction functions. 1. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. 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. Complete the 4-day Level 1 Basic CPTED Course. k. Src faceset is celebrity. Phase II: Training. It is used at 2 places. 1. Timothy B. 1256. Again, we will use the default settings. If it is successful, then the training preview window will open. 1. Use XSeg for masking. Training XSeg is a tiny part of the entire process. SAEHD is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020. Tensorflow-gpu 2. 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 face. First one-cycle training with batch size 64. XSeg-prd: uses. CryptoHow to pretrain models for DeepFaceLab deepfakes. 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. py","path":"models/Model_XSeg/Model. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. 000 it) and SAEHD training (only 80. #4. Double-click the file labeled ‘6) train Quick96. **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. learned-prd*dst: combines both masks, smaller size of both. 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. . . Pickle is a good way to go: import pickle as pkl #to save it with open ("train. Read the FAQs and search the forum before posting a new topic. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. 3. Keep shape of source faces. Where people create machine learning projects. 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. , train_step_batch_size), the gradient accumulation steps (a. Choose the same as your deepfake model. The software will load all our images files and attempt to run the first iteration of our training. I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. Increased page file to 60 gigs, and it started. bat. . Must be diverse enough in yaw, light and shadow conditions. The Xseg training on src ended up being at worst 5 pixels over. 0 using XSeg mask training (213. 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. Download Celebrity Facesets for DeepFaceLab deepfakes. DLF installation functions. 05 and 0. workspace. Use the 5. Windows 10 V 1909 Build 18363. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. . 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. In this video I explain what they are and how to use them. 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-dst: uses trained XSeg model to mask using data from destination faces. However, I noticed in many frames it was just straight up not replacing any of the frames. Xseg editor and overlays. Where people create machine learning projects. I'm facing the same problem. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. Xseg apply/remove functions. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. I have an Issue with Xseg training. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. Where people create machine learning projects. Manually labeling/fixing frames and training the face model takes the bulk of the time. XSeg) data_dst trained mask - apply or 5. 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. It depends on the shape, colour and size of the glasses frame, I guess. Easy Deepfake tutorial for beginners Xseg. resolution: 128: Increasing resolution requires significant VRAM increase: face_type: f: learn_mask: y: optimizer_mode: 2 or 3: Modes 2/3 place work on the gpu and system memory. S. Model training is consumed, if prompts OOM. load (f) If your dataset is huge, I would recommend check out hdf5 as @Lukasz Tracewski mentioned. Hello, after this new updates, DFL is only worst. . added 5. 0 XSeg Models and Datasets Sharing Thread. Where people create machine learning projects. Step 5: Training. But I have weak training. 4. 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;. caro_kann; Dec 24, 2021; Replies 6 Views 3K. Where people create machine learning projects. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. dump ( [train_x, train_y], f) #to load it with open ("train. Xseg Training is a completely different training from Regular training or Pre - Training. 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. pkl", "w") as f: pkl. Instead of using a pretrained model. I could have literally started merging after about 3-4 hours (on a somewhat slower AMD integrated GPU). 522 it) and SAEHD training (534. ProTip! Adding no:label will show everything without a label. npy","contentType":"file"},{"name":"3DFAN. After training starts, memory usage returns to normal (24/32). This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Does the model differ if one is xseg-trained-mask applied while. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 1 participant. 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,. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. Pretrained models can save you a lot of time. after that just use the command. With the first 30. 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. 3: XSeg Mask Labeling & XSeg Model Training Q1: XSeg is not mandatory because the faces have a default mask. ogt. both data_src and data_dst. It must work if it does for others, you must be doing something wrong. 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. . I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. Change: 5. How to share XSeg Models: 1. Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. The software will load all our images files and attempt to run the first iteration of our training. 000 it). The software will load all our images files and attempt to run the first iteration of our training. The images in question are the bottom right and the image two above that. Manually fix any that are not masked properly and then add those to the training set. 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). npy","path":"facelib/2DFAN. The fetch. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. Where people create machine learning projects. You can see one of my friend in Princess Leia ;-) I've put same scenes with different. 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) data_src trained mask - apply the CMD returns this to me. 0146. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 3. Business, Economics, and Finance. oneduality • 4 yr. Basically whatever xseg images you put in the trainer will shell out. 6) Apply trained XSeg mask for src and dst headsets. py","path":"models/Model_XSeg/Model. How to share SAEHD Models: 1. You can use pretrained model for head. 2) extract images from video data_src. . Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Step 1: Frame Extraction. 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. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.