casd's People
Forkers
houlw-cacas yzou2 liujianlun lliai chestnut-fish mrk1992 bityangke lukpcm jinhseo cv-ip gaobb jlu2016 yinglang hwijune fuuuyuuu tmlabonte n8srumsey qu-zhenyu aliuyb yuzima whuhxbcasd's Issues
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=383 error=11 : invalid argument
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=383 error=11 : invalid argument
Traceback (most recent call last):
File "./tools/trainval_net.py", line 135, in
max_iters=args.max_iters)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/model/train_val.py", line 377, in train_net
sw.train_model(max_iters)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/model/train_val.py", line 291, in train_model
cls_det_loss, refine_loss_1, refine_loss_2, consistency_loss, total_loss = self.net.train_step(blobs,self.optimizer,iter)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/nets/network.py", line 634, in train_step
self.forward(blobs['data'], blobs['image_level_labels'], blobs['im_info'], blobs['gt_boxes'], blobs['ss_boxes'], step)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/nets/network.py", line 562, in forward
roi_labels_1, keep_inds_1, roi_labels_2, keep_inds_2, bbox_pred, rois = self._predict_train(ss_boxes_all, step)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/nets/network.py", line 508, in _predict_train
roi_labels_2, keep_inds_2, bbox_pred = self._region_classification_train(pool5_roi, fc7_roi,fc7_context, fc7_frame, step)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/nets/network.py", line 398, in _region_classification_train
mask_1 = self._inverted_attention(bbox_feats_new, gt, keep_inds_1_new, 1, step, fg_num_1_new, bg_num_1_new)
File "/hy-tmp/import_datasets/CASD-master_2022-01-20-210309/CASD-master/tools/../lib/nets/network.py", line 147, in _inverted_attention
pooled_feat_before_after = torch.cat((bbox_feats_new, bbox_feats_new * mask_all), dim=0)
RuntimeError: CUDA out of memory. Tried to allocate 766.00 MiB (GPU 0; 10.76 GiB total capacity; 9.17 GiB already allocated; 658.56 MiB free; 118.66 MiB cached)
CUDA out of memory
when i run, i got "RuntimeError: CUDA out of memory."i don't know how to modify.
gpu0: 12G; gpu1: 12G
my command is "bash experiments/scripts/train_faster_rcnn.sh 0 pascal_voc vgg16" or
"bash experiments/scripts/train_faster_rcnn.sh 0,1 pascal_voc vgg16". The two commands both caused "RuntimeError: CUDA out of memory"
And i modified vgg16.yml. TRAIN.BATCH_SIZE : 256 --> 2
Running Logs:
- set -e
- export PYTHONUNBUFFERED=True
- PYTHONUNBUFFERED=True
- GPU_ID=0
- DATASET=pascal_voc
- NET=vgg16
- array=($@)
- len=3
- EXTRA_ARGS=
- EXTRA_ARGS_SLUG=
- case ${DATASET} in
- TRAIN_IMDB=voc_2007_trainval
- TEST_IMDB=voc_2007_test
- STEPSIZE='[50000]'
- ITERS=100000
- ANCHORS='[8,16,32]'
- RATIOS='[0.5,1,2]'
++ date +%Y-%m-%d_%H-%M-%S - LOG=experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2021-09-11_15-11-21
- exec
++ tee -a experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2021-09-11_15-11-21
tee: experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2021-09-11_15-11-21: No such file or directory - echo Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2021-09-11_15-11-21
Logging output to experiments/logs/vgg16_voc_2007_trainval__vgg16.txt.2021-09-11_15-11-21 - set +x
- '[' '!' -f output/vgg16/voc_2007_trainval/default/vgg16_MELM_iter_100000.pth.index ']'
- [[ ! -z '' ]]
- CUDA_VISIBLE_DEVICES=0
- python ./tools/trainval_net.py --weight data/imagenet_weights/vgg16.pth --imdb voc_2007_trainval --imdbval voc_2007_test --iters 100000 --cfg experiments/cfgs/vgg16.yml --net vgg16 --set ANCHOR_SCALES '[8,16,32]' ANCHOR_RATIOS '[0.5,1,2]' TRAIN.STEPSIZE '[50000]'
Called with args:
Namespace(cfg_file='experiments/cfgs/vgg16.yml', imdb_name='voc_2007_trainval', imdbval_name='voc_2007_test', max_iters=100000, net='vgg16', set_cfgs=['ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'TRAIN.STEPSIZE', '[50000]'], tag=None, weight='data/imagenet_weights/vgg16.pth')
/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/model/config.py:369: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
yaml_cfg = edict(yaml.load(f))
Loaded datasetvoc_2007_trainval
for training
Set proposal method: selective_search
Appending horizontally-flipped training examples...
voc_2007_trainval ss roidb loaded from /media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/data/cache/voc_2007_trainval_selective_search_roidb.pkl
done
Preparing training data...
done
10022 roidb entries
Output will be saved to/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/output/vgg16_MELM/voc_2007_trainval/default
TensorFlow summaries will be saved to/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tensorboard/vgg16_MELM/voc_2007_trainval/default
Loaded datasetvoc_2007_test
for training
Set proposal method: selective_search
Preparing training data...
voc_2007_test ss roidb loaded from /media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/data/cache/voc_2007_test_selective_search_roidb.pkl
done
4952 validation roidb entries
Filtered 0 roidb entries: 10022 -> 10022
Filtered 0 roidb entries: 4952 -> 4952
Solving...
Loading initial model weights from data/imagenet_weights/vgg16.pth
Loaded.
Traceback (most recent call last):
File "./tools/trainval_net.py", line 135, in
max_iters=args.max_iters)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/model/train_val.py", line 377, in train_net
sw.train_model(max_iters)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/model/train_val.py", line 291, in train_model
cls_det_loss, refine_loss_1, refine_loss_2, consistency_loss, total_loss = self.net.train_step(blobs,self.optimizer,iter)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/nets/network.py", line 634, in train_step
self.forward(blobs['data'], blobs['image_level_labels'], blobs['im_info'], blobs['gt_boxes'], blobs['ss_boxes'], step)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/nets/network.py", line 562, in forward
roi_labels_1, keep_inds_1, roi_labels_2, keep_inds_2, bbox_pred, rois = self._predict_train(ss_boxes_all, step)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/nets/network.py", line 508, in _predict_train
roi_labels_2, keep_inds_2, bbox_pred = self._region_classification_train(pool5_roi, fc7_roi,fc7_context, fc7_frame, step)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/nets/network.py", line 398, in _region_classification_train
mask_1 = self._inverted_attention(bbox_feats_new, gt, keep_inds_1_new, 1, step, fg_num_1_new, bg_num_1_new)
File "/media/omnisky/28db8425-dc36-4700-92ef-0dd7e98ccd67/djt/CASD/tools/../lib/nets/network.py", line 147, in _inverted_attention
pooled_feat_before_after = torch.cat((bbox_feats_new, bbox_feats_new * mask_all), dim=0)
RuntimeError: CUDA out of memory. Tried to allocate 766.00 MiB (GPU 0; 11.91 GiB total capacity; 9.59 GiB already allocated; 99.19 MiB free; 1.49 GiB cached)
I would appreciate it if you could help me.
ValueError: zero-size array to reduction operation maximum which has no identity
when i run this code, there occurred an error:'ValueError: zero-size array to reduction operation maximum which has no identity.' what should i do?
Issue with CUDA11.1
Selective search data not found at: /sequoia/data2/vavo/wsod/CASD/data/selective_search_data/voc_2007_trainval.mat
Hi, thank you for your interesting work.
I tried to set up the code for running. I downloaded the selective search data from the provided link and put them in data/selective_search_data but ran into this error:
Traceback (most recent call last):
File "./tools/trainval_net.py", line 109, in <module>
imdb, roidb = combined_roidb(args.imdb_name)
File "./tools/trainval_net.py", line 74, in combined_roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
File "./tools/trainval_net.py", line 74, in <listcomp>
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
File "./tools/trainval_net.py", line 71, in get_roidb
roidb = get_training_roidb(imdb)
File "/sequoia/data2/vavo/wsod/CASD/tools/../lib/model/train_val.py", line 331, in get_training_roidb
imdb.append_flipped_images()
File "/sequoia/data2/vavo/wsod/CASD/tools/../lib/datasets/imdb.py", line 113, in append_flipped_images
boxes = self.roidb[i]['boxes'].copy()
File "/sequoia/data2/vavo/wsod/CASD/tools/../lib/datasets/imdb.py", line 74, in roidb
self._roidb = self.roidb_handler()
File "/sequoia/data2/vavo/wsod/CASD/tools/../lib/datasets/pascal_voc.py", line 142, in selective_search_roidb
ss_roidb = self._load_selective_search_roidb(gt_roidb)
File "/sequoia/data2/vavo/wsod/CASD/tools/../lib/datasets/pascal_voc.py", line 176, in _load_selective_search_roidb
'Selective search data not found at: {}'.format(filename)
AssertionError: Selective search data not found at: /sequoia/data2/vavo/wsod/CASD/data/selective_search_data/voc_2007_trainval.mat
Do we need to transform the .pkl files to .mat files? Can you provide the code for the transformation please?
ImportError: libcudart.so.9.0: cannot open shared object file: No such file or directory
Thanks for sharing your great work. But when I run the program, I met an error.
File "./tools/trainval_net.py", line 21, in
from nets.vgg16 import MELM_vgg16
File "/home/username/CASD/tools/../lib/nets/vgg16.py", line 10, in
from nets.network import Network
File "/home/username/CASD/tools/../lib/nets/network.py", line 18, in
from ops.roi_pool import RoIPool
File "/home/username/CASD/tools/../lib/ops/init.py", line 9, in
from .nms import nms, soft_nms
File "/home/username/CASD/tools/../lib/ops/nms/init.py", line 1, in
from .nms_wrapper import nms, soft_nms
File "/home/username/CASD/tools/../lib/ops/nms/nms_wrapper.py", line 4, in
from . import nms_cpu, nms_cuda
ImportError: libcudart.so.9.0: cannot open shared object file: No such file or directory
My Environment: python ==3.7.9, pytorch==1.1.0, torchvision == 0.3.0, cuda ==10.0
Do you have any solution to this error?
Usability of BDD100K dataset
Greetings,
This is Aman Goyal. I am currently pursuing research in MSU in the domain of knowledge distillation and I had come across your paper and github repo.
I actually wanted to train on BDD100K detection dataset. Is it possible to integrate with your codebase ?
Regards,
Aman Goyal
Question about consistency loss!!!
Hi~
Does consistency_conf_loss really work?
Firstly, I run your code as you stated in README.md and found the fact that consistency_conf_loss is really small. So I guess that consistency_conf_loss may have no contribution to the performance. Then I tried remove the consistency_conf_loss and run your "baseline" again. I got nearly the same performance 53+ even when the training process had not been finished.
I wonder that the really important part of your codebase is your tricks which are not mentioned in your paper or the consistency_conf_loss.
where to get the file 'voc_2007_trainval.mat'?
thanks for your great work!
when I run the codes, there occurred an error: 'Selective search data not found at: data/selective_search_data/voc_2007_trainval.mat', I want to know where or how to get this file?
The memory of the code
Hi,Nice work!
Thanks for releasing the code, I have a question about the GPU memory of this code. I ran the default parameter to train the network, like bs=1, net=vgg16. I have seen that the required GPU memory was 24G,When I used four 1080ti to train the network, I met the out of memory error, the memory of 1080ti is 11G. When are you going to release code of mutil gpu training ?
Thank you very much!
Some issues about proposal files
Hello, your work is excellent! Proposal files provided by you contain four .pkl files. However, codes seemingly needs .mat file. Whether are proposal files incorrect? Thank you very much! And when I directly load .pkl file as proposals, there exits 'gt_classes', 'image_level_labels' and so on. However, I can not find these in your provided .pkl files. I do not know why. Can you provide the .mat file? Thank you very much!
hi,your work is very interesting (●'◡'●)
Hope you can open your code !
Best wishes for you~
Question about the attention map in the paper
cu:259
Excuse you.
My environment configuration is: 2080ti+cuda9.0+pytorch1.1.0+torchvision 0.3.0
The following problems occur:
Traceback (most recent call last):
File "./tools/trainval_net.py", line 138, in
max_iters=args.max_iters)
File "/disk1/summer/code/CASD/tools/../lib/model/train_val.py", line 377, in train_net
sw.train_model(max_iters)
File "/disk1/summer/code/CASD/tools/../lib/model/train_val.py", line 277, in train_model
self.net.train_step_with_summary(blobs, self.optimizer, iter)
File "/disk1/summer/code/CASD/tools/../lib/nets/network.py", line 651, in train_step_with_summary
self.forward(blobs['data'], blobs['image_level_labels'], blobs['im_info'], blobs['gt_boxes'], blobs['ss_boxes'], step)
File "/disk1/summer/code/CASD/tools/../lib/nets/network.py", line 563, in forward
roi_labels_1, keep_inds_1, roi_labels_2, keep_inds_2, bbox_pred, rois = self._predict_train(ss_boxes_all, step)
File "/disk1/summer/code/CASD/tools/../lib/nets/network.py", line 500, in _predict_train
fc7_roi = self._head_to_tail(pool5_roi)
File "/disk1/summer/code/CASD/tools/../lib/nets/vgg16.py", line 58, in _head_to_tail
fc7 = self.vgg.classifier(pool5_flat)
File "/home/summer/anaconda3/envs/CASD/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/summer/anaconda3/envs/CASD/lib/python3.7/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/home/summer/anaconda3/envs/CASD/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/summer/anaconda3/envs/CASD/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 92, in forward
return F.linear(input, self.weight, self.bias)
File "/home/summer/anaconda3/envs/CASD/lib/python3.7/site-packages/torch/nn/functional.py", line 1406, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653215914/work/aten/src/THC/THCBlas.cu:259
Do you have any suggestions ?Thanks.
importerror
I want to train it by:
bash experiments/scripts/train_faster_rcnn.sh 0,1,2,3 pascal_voc vgg16
but found an error:
Traceback (most recent call last):
File "./tools/trainval_net.py", line 21, in
from nets.vgg16 import MELM_vgg16
File "/home/share/CASD/tools/../lib/nets/vgg16.py", line 10, in
from nets.network import Network
File "/home/share/CASD/tools/../lib/nets/network.py", line 18, in
from ops.roi_pool import RoIPool
File "/home/share/CASD/tools/../lib/ops/init.py", line 13, in
from .roi_ring_pool import RoIRingPool
File "/home/share/CASD/tools/../lib/ops/roi_ring_pool/init.py", line 9, in
from .roi_ring_pool import RoIRingPool
File "/home/share/CASD/tools/../lib/ops/roi_ring_pool/roi_ring_pool.py", line 13, in
from . import roi_ring_pool_cuda
ImportError: libcudart.so.10.1: cannot open shared object file: No such file or directory
it makes my crazy,how to solve it (ㄒoㄒ)
ImportError: cannot import name 'nms_cpu' from partially initialized module 'lib.ops.nms' (most likely due to a circular import) (F:\多目标弱监督\CASD-master\lib\ops\nms\__init__.py)
Has anyone encountered this problem and how to solve it
The mAP on VOC2007 test in only 53.5%, which is much worse than 56.8% in the Neurips paper
Hello, your work is excellent!
But the result of your code is only 53.5% mAP, which is significantly lower than 56.8% mAP reported in your paper.
Any suggestions to how one might reproduce the published results?
the classification AP is
AP for aeroplane = 0.9836
AP for bicycle = 0.9709
AP for bird = 0.9765
AP for boat = 0.9553
AP for bottle = 0.8009
AP for bus = 0.9501
AP for car = 0.9724
AP for cat = 0.9677
AP for chair = 0.7514
AP for cow = 0.9229
AP for diningtable = 0.8283
AP for dog = 0.9770
AP for horse = 0.9687
AP for motorbike = 0.9549
AP for person = 0.9897
AP for pottedplant = 0.8265
AP for sheep = 0.9278
AP for sofa = 0.8116
AP for train = 0.9726
AP for tvmonitor = 0.9102
__________________
the mAP is 92.0957
Evaluating detections
Writing aeroplane VOC results file
Writing bicycle VOC results file
Writing bird VOC results file
Writing boat VOC results file
Writing bottle VOC results file
Writing bus VOC results file
Writing car VOC results file
Writing cat VOC results file
Writing chair VOC results file
Writing cow VOC results file
Writing diningtable VOC results file
Writing dog VOC results file
Writing horse VOC results file
Writing motorbike VOC results file
Writing person VOC results file
Writing pottedplant VOC results file
Writing sheep VOC results file
Writing sofa VOC results file
Writing train VOC results file
Writing tvmonitor VOC results file
VOC07 metric? Yes
AP for aeroplane = 0.6933
AP for bicycle = 0.6773
AP for bird = 0.5468
AP for boat = 0.3945
AP for bottle = 0.2312
AP for bus = 0.6964
AP for car = 0.6922
AP for cat = 0.7217
AP for chair = 0.2173
AP for cow = 0.6577
AP for diningtable = 0.4632
AP for dog = 0.6464
AP for horse = 0.5931
AP for motorbike = 0.6789
AP for person = 0.1639
AP for pottedplant = 0.2502
AP for sheep = 0.5352
AP for sofa = 0.5645
AP for train = 0.6312
AP for tvmonitor = 0.6608
Mean AP = 0.5358
Query about proposal selection for CASD
Hey, super interesting work. I was wondering, your paper says that N_K positive and negative proposals are selected as in OICR, and then for all of those proposals you compute your CASD attention maps/losses. OICR does no subsampling of proposals, all selective search proposals are labelled as either positive or negative and are backpropogated. Does this mean that you actually just use them all? Or do you use some other method (e.g. only the pseudo ground truths, only the positives, or using an ignore threshold as in Tang et. al. ECCV2018)?
Can't find the layer-wise CASD code in both master and may30
selective search code
Hi!I want to train the model with my own dataset,could you provide the code of generating selective search box?thank you very much
About consistency loss
Thank you for your outstanding work.
During the training of the network, self.ca_iw
is always False, I noticed that in network.py#L216, self.ca_iw
is modified to False, but when iteration> 45000
, self.ca_iw
also seems to be False. The result of my training is worse than the result in the paper. Is it because ca_iw
has not been used? The return value rampweight of the function that modifies self.ca_iw
is also not used in the subsequent code, so I am not too clear about the logic of when to use self.ca_iw
.
Hi, my bro. Why layer-wise CASD is still not added into the project CASD-may30 ? Thank you.
The total loss has not contained the layer-wise loss.
Hi, I want to know why not set that self.ca_iw is true in the begining. Looking forward to your reply. Thank you very much!
Hi,
In line216, you can find that self.ca_iw is false at the first several epochs, then it is set to true. I will complete the paralleled code soon.
Originally posted by @Justinhzy in #13 (comment)
Correction in README.md
Hi, thank you for the interesting work. I was working on a WSOD Project and found your paper to be really interesting and was implementing it for my project. While implementing it, I found a small error in the README i.e. :
While downloading the VOCdevkit you have mentioned the 18-May-2011 version to be downloaded:
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar
But while extracting it, you have instead extracted the 8-Jun-2007 version:
tar xvf VOCdevkit_08-Jun-2007.tar
Even though it is not a significant mistake, I thought updating it might help others and make it more complete. Nevertheless, thank you for the great work!
cudnn version
Hi! thanks for sharing your great work. When I run the program, I met an error:
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED.
So could you tell me the version of cudnn you use? I try 7.6.5 and 7.6.4, but I met the same error. Thanks!
ImportError: cannot import name 'roi_pool_cuda' from 'ops.roi_pool'
Where is this file?Is it roi_pool_cuda.cpython-37m-x86_64-linux-gnu.so this file?
Hi my bro. When will the code supporting parallelism be released ? And where is the layer-wise loss in code?Thank you!
About the implementation for bbr reg head.
Hello! I added the bbr regression head on your novel model but the result was not good. Could you share the code of this part, or explain the detail?
Thanks a lot!!
Question about cls_det_loss & when will the code supporting parallelism be released.
Hi, CASD is really a great job which could get perfect performance.
However, when will the code supporting parallelism be released.
And, could you tell me the performace when remove tricks that are not used in original OICR.
Thanks a lot ~
I am also confused with the cls_det_loss. It seems like different from the normal MIL loss, could you please tell more about that. -> Why using self._predictions['det_cls_prob']
which is computed with the cls_score before softmax.
Thanks~
About the training speed
Thanks for your great job!
I train your model on TITAN RTX and the speed is about 2.7s/iter.
Is the speed the same as yours? Or maybe it’s a problem with my GPU.
Looking forward to your reply.
ValueError: unsupported pickle protocol: 5
bash experiments/scripts/train_faster_rcnn.sh 0 pascal_voc vgg16
inverted attention
Hi, I think your work is very interesting, I wonder how to use Inverted Attention (IA) in OICR, thank you!
why self.ca_iw always be False in the code?
Line 218 in d95a16a
Here self.ca_iw set to be False, and I can not see any codes setting it to be True, does it mean the consistence loss is always zero during training? Am I got something wrong or the code is not complete?
Why is self.ca_iw initialized to False in network.py of the project CASD-may 30? Thank you!
It is different from the project CASD-master. Does it mean that the project CASD-may 30 has not used the input-wise loss? Thank you.
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.