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View Code? Open in Web Editor NEWSFD implement with pytorch
SFD implement with pytorch
Hi, glad to see your update.
I saw this issue a few days ago, did you figure out what way the key?
sfzhang15/SFD#57
Hi, thank you very much for your open source code.
However, I run the 'demo.py' and find the running on one frame using RTX 3080 is about 0.5 seconds. The original paper claimed this method is real-time.
Could you please give me some advice on this?
I am finding it real hard to download checkpoint files from the website you uploaded in as I do not understand Chinese.
Is there any other way you can share them? Much appreciated. Thanks.
Hi,
Thanks for providing the code and pretrained model. However, when I ran the wider_test.py, the meanAP was not shown. And I only found eval_hand.py and eval_head.py in tools/.
Would you please offer the evaluation file of wider face so that we can compare the results with the paper?
anchor_matching_test.py 中 的S3FDValTransform有么?
Hi,
Thanks for sharing the code!
Here are my questions:
您好,您有尝试将VGG16替换为Res50吗?我之前复现了SFD(F)部分,,test时没有用多尺度,hard子集可以达到79.3%.但替换Res50后,准确率反而下降到了73.6%.想请教一下您这是啥原因.Res50直接用的trochvision里的预训练模型.
另外您的QQ需要真实姓名验证才能加.
I ran wider_test.py and got a folder —— s3fd_val in eval_tools.
Then I ran wider_eval.m in MATLAB, but some errors were reported.
错误使用 legend (line 273)
未知的属性 'show'
出错 plot_pr (line 22)
legend1 = legend(lendge_name,'show');
出错 wider_plot (line 33)
plot_pr(propose, recall, name_list, seting_class, set_list{i},dateset_class);
出错 wider_eval (line 35)
wider_plot(setting_name_list,dir_int,seting_class,dateset_class);
Hi, Could you please provide the pretrained weight file for demo.py, so that I can test it on any image? Also the basenet vgg16_reducedfc.pth for training?
Thanks.
is there any way to run this on raspberry pi device
what would be the approx fps on it
What is the license for this code? Could you please clarify so that we decide whether or not to use this code?
This model takes 500+ GFLOPS, it is very hard to land in industry world.
...由于之前没有做过目标检测,望解答...
It's a problem to download something from pan.baidu.com. May I ask you for uploading pretrained face model on some open resource?
How to judge that there is no face?
HI. I am trying to run the code.
Which parts in config.py should be modified?
Error when running prepare_wider_data.py, can not find text file "wider_face_train_bbx_gt.txt".
What is this file and where can I obtain it.
Thank you.
Hi, is it compatible with your code, do you have any advice on this? Thanks!
def nms(boxes, scores, overlap=0.5, top_k=200):
keep = scores.new(scores.size(0)).zero_().long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w * h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter / union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
**_return keep, count_**
the two return are not same ,it may take error:
layers/functions/detection.py", line 67, in forward
ValueError: not enough values to unpack (expected 2, got 0)
Whenever i am passing the images to the model,the memory consumption of RAM is increasing heavily. So is there any way to reduce it? or else this model itself need more configurations?
Thanks in advance.
when I download the model you provided ,and test on GPU ,the inference time per_image is about 0.75s ,the picture size is 640*480
Is there a pretrained weight file ?
Hello, I'm a student of UESTC,and I saw your code these days ,and I want to ask you some questions and learn from you
Thanks for your code
in the paper
M = num_pos.data.sum().float() + num_neg.data.sum().float()
loss_c /= N
also in the paper L =\lambda * L_{cls} + L_{reg}
in the code
loss_l, loss_c = criterion(out, targets)
loss = loss_l + loss_c
may be the two place should be modified
Hi, I have downloaded your ''face model'' which is provided in README, and successfully run it on my machine. But now I still have some questions because it seems works well on faces but not work well on heads.
Here is my question: Is the "face model" trained with both head dataset "SCUT-HEAD" and face dataset "widerface", or just only trained with face dataset "widerface"?
您好,向您请教一个额外的问题.
wider face那篇论文中说忽视10像素以下的人脸,但实际标注的人脸有很多低于10像素的,甚至有2个像素的.HR那篇论文中也说人脸像素大于10.
请问是wider face数据集是以前没有标注低于10像素的么?还是说训练时忽视掉低于10像素的人脸?
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