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View Code? Open in Web Editor NEWA prize winning solution for DFDC challenge
License: MIT License
A prize winning solution for DFDC challenge
License: MIT License
Why are you multiplying the coordinates of bounding box by 2? It will shift the region that we want to crop. I think it should not be multiplied.
Thanks.
I visualized this part of the code and found that it didn't work.
Can you check it out, or open source visual code ?
Thank you very much !
When I run generate_fold.py,I find the "fold" is alway "0"
for k, v in metadata.items():
fold = None
for i, fold_dirs in enumerate(folds):
#if part in fold_dirs:
fold = i
break
assert fold is not None
video_id = k[:-4]
video_fold[video_id] = fold
I debug this part and find "fold” is alway "0" and then break
Since I can't understand what does "fold" mean,I don't know how to solve it and what is the correct
could you explain it in detail? thank you!
Как насчёт улучшать дипфейки, а не детектить их? :D
Timm library updated version causes trouble in the code.
dfdc_deepfake_challenge/Dockerfile
Line 59 in 9925d95
Installing an older version resolved the issue.
pip install timm==0.1.26
--2020-11-02 14:27:42-- https://github.com/selimsef/dfdc_deepfake_challenge/releases/download//final_999_DeepFakeClassifier_tf_efficientnet_b7_ns_0_23
Resolving github.com (github.com)... 15.164.81.167
Connecting to github.com (github.com)|15.164.81.167|:443... connected.
HTTP request sent, awaiting response... 404 Not Found
2020-11-02 14:27:42 ERROR 404: Not Found.
i encontered this issue. What can be done to solve this problem.
Hello! I need to ask, Is it possible to run this code on Google Colab?
When I ran python preprocessing/detect_original_faces.py --root-dir DATA_ROOT
, I encountered an error:
Traceback (most recent call last):
File "preprocessing/detect_original_faces.py", line 14, in
import face_detector, VideoDataset
ModuleNotFoundError: No module named 'VideoDataset'
How can I solve this problem?
I guess this should be mid_h = h // 2
.
looking forward to that
Hi Selim,
Thank you for sharing your great work here, tried to use your predict_submission.sh to reproduce the submission.csv by using 7 efficientnet-b7 models on test_videos folder, but the prediction scores for all 400 videos are smaller than 0.5, most of them around 0.3-0.4, guess I did something wrong here, but just cannot figure out what could be the possible reasons, can help here?
Xu
Hi,
I just have a quick question. You mention that you resize the videos before the face detector.
Do we need to resize the videos before we run preprocess_data.sh?
Or preprocess_data.sh would also handle the resize of the videos as well when we run face detector?
I can not find the code you resize the image. This is the closest thing I find in your code.
Thank you!
In face_detector.py :
self.detector = MTCNN(margin=0,thresholds=[0.85, 0.95, 0.95], device=device)
but in kernel_utils.py :
self.detector = MTCNN(margin=0, thresholds=[0.7, 0.8, 0.8], device="cuda")
this is why ?
thank you
./train.sh path/to/data num-of-gpus
100%██████████████████████████████████████████████████████████████████████████████████| 2765/2765 [06:11<00:00, 7.45it/s]
Traceback (most recent call last):
File "training/pipelines/train_classifier.py", line 363, in
main()
File "training/pipelines/train_classifier.py", line 227, in main
summary_writer=summary_writer)
File "training/pipelines/train_classifier.py", line 235, in evaluate_val
bce, probs, targets = validate(model, data_loader=data_val)
File "training/pipelines/train_classifier.py", line 290, in validate
real_loss = log_loss(y[real_idx], x[real_idx], labels=[0, 1])
File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py", line 2186, in log_loss
y_pred = check_array(y_pred, ensure_2d=False)
File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 72, in inner_f
return f(**kwargs)
File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 653, in check_array
context))
ValueError: Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.
I encountered this issue during validation
File "finetune_xy.py", line 446, in <module>
main()
File "finetune_xy.py", line 303, in main
summary_writer=summary_writer)
File "finetune_xy.py", line 311, in evaluate_val
bce, probs, targets = validate(model, data_loader=data_val)
File "finetune_xy.py", line 366, in validate
fake_loss = log_loss(y[fake_idx], x[fake_idx], labels=[0, 1])
File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 73, in inner_f
return f(**kwargs)
File "/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py", line 2206, in log_loss
transformed_labels = lb.transform(y_true)
File "/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_label.py", line 491, in transform
sparse_output=self.sparse_output)
File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 73, in inner_f
return f(**kwargs)
File "/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_label.py", line 680, in label_binarize
"binarization" % y_type)
ValueError: continuous target data is not supported with label binarization
[1]+ Exit 1 nohup python -u finetune_xy.py --config configs/b7.json > log.out
could you explain a little bit the
data_x = []
data_y = []
for vid, score in probs.items():
score = np.array(score)
lbl = targets[vid]
score = np.mean(score)
lbl = np.mean(lbl)
data_x.append(score)
data_y.append(lbl)
y = np.array(data_y)
x = np.array(data_x)
fake_idx = y > 0.1
real_idx = y < 0.1
fake_loss = log_loss(y[fake_idx], x[fake_idx], labels=[0, 1])
real_loss = log_loss(y[real_idx], x[real_idx], labels=[0, 1])
print("{}fake_loss".format(prefix), fake_loss)
print("{}real_loss".format(prefix), real_loss)
in your code? Thank you
Is there a way of using the trained weights & do inference using CPU only? My GPU can't handle inference with the current settings...
Hi! Thank you for your work. Does this detect face filters like you would see on instagram, for example how people use for beauty purposes, like to add makeup to the face, or change the face in general? Or does it strictly detect deepfakes?
Could you talk about it in detail ?
Thank you !
https://ai.facebook.com/blog/deepfake-detection-challenge-results-an-open-initiative-to-advance-ai/
the webpage said that the top accuracy in private evaluate dataset is about 65.18%, but I evalute the model you provided, got an accuracy about 89%, what's wrong?
looking forward for any reply.
Thank you for your work. What training data do you use? All dfdc?Your model is basically unrecognizable on FOMM video. I want to add this batch of data to train your model.
Hi, there.
I'm trying to preprocess your model, however, I had an issue running generate_folds.py
Here is the screenshot of the error.
Run it multiple times then it shows different video file names.
I looked up the metadata.json, I guess the error is from the training video have no original video.
It is means the assert videofold[video] will not equal to videofold[ori_vid].
I not should how to fix this.
Hope to hear from you soon.
Thanks,
Silion
I have downloaded the DFDC dataset, but metadata.json wasn't found, can you share the download link of this file, thank you very much
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