🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

Last update: Dec 26, 2022

Overview

🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ?

If you hate the fucking tensorflow1.x very much, no worries! I have implemented a new YOLOv3 repo with TF2.0, and also made a chinese blog on how to implement YOLOv3 object detector from scratch.
code | blog | issue

part 1. Quick start

Clone this file

$ git clone https://github.com/YunYang1994/tensorflow-yolov3.git

You are supposed to install some dependencies before getting out hands with these codes.

$ cd tensorflow-yolov3
$ pip install -r ./docs/requirements.txt

Exporting loaded COCO weights as TF checkpoint(yolov3_coco.ckpt)【BaiduCloud】

$ cd checkpoint
$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz
$ tar -xvf yolov3_coco.tar.gz
$ cd ..
$ python convert_weight.py
$ python freeze_graph.py

Then you will get some .pb files in the root path., and run the demo script

$ python image_demo.py
$ python video_demo.py # if use camera, set video_path = 0

part 2. Train your own dataset

Two files are required as follows:

dataset.txt:

xxx/xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20 
xxx/xxx.jpg 48,240,195,371,11 8,12,352,498,14
# image_path x_min, y_min, x_max, y_max, class_id  x_min, y_min ,..., class_id 
# make sure that x_max < width and y_max < height

class.names:

person
bicycle
car
...
toothbrush

2.1 Train on VOC dataset

Download VOC PASCAL trainval and test data

$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

Extract all of these tars into one directory and rename them, which should have the following basic structure.


VOC           # path:  /home/yang/dataset/VOC
├── test
|    └──VOCdevkit
|        └──VOC2007 (from VOCtest_06-Nov-2007.tar)
└── train
     └──VOCdevkit
         └──VOC2007 (from VOCtrainval_06-Nov-2007.tar)
         └──VOC2012 (from VOCtrainval_11-May-2012.tar)
                     
$ python scripts/voc_annotation.py --data_path /home/yang/test/VOC

Then edit your ./core/config.py to make some necessary configurations

__C.YOLO.CLASSES                = "./data/classes/voc.names"
__C.TRAIN.ANNOT_PATH            = "./data/dataset/voc_train.txt"
__C.TEST.ANNOT_PATH             = "./data/dataset/voc_test.txt"

Here are two kinds of training method:

(1) train from scratch:

$ python train.py
$ tensorboard --logdir ./data

(2) train from COCO weights(recommend):

$ cd checkpoint
$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz
$ tar -xvf yolov3_coco.tar.gz
$ cd ..
$ python convert_weight.py --train_from_coco
$ python train.py

2.2 Evaluate on VOC dataset

$ python evaluate.py
$ cd mAP
$ python main.py -na

the mAP on the VOC2012 dataset:

part 3. Other Implementations

-YOLOv3目标检测有了TensorFlow实现，可用自己的数据来训练

-Stronger-yolo

- Implementing YOLO v3 in Tensorflow (TF-Slim)

- YOLOv3_TensorFlow

- Object Detection using YOLOv2 on Pascal VOC2012

-Understanding YOLO

Comments

训练几个echoes之后，会出错IndexError: index 77 is out of bounds for axis 0 with size 76

在我运行训练的时候前几个echoes都跑到好好的，但是下一个echoe就会出错 File "/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py", line 930, in iter for obj in iterable: File "/workspace/tensorflow-yolov3-master/core/dataset.py", line 76, in next label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes) File "/workspace/tensorflow-yolov3-master/core/dataset.py", line 234, in preprocess_true_boxes label[i][yind, xind, iou_mask, :] = 0 IndexError: index 77 is out of bounds for axis 0 with size 76 请问这是什么问题@YunYang1994

opened by CNUyue 11

中断模型训练后，如何接着中断的地方继续训练？

各位前辈好！请问运行train.py的时候，比如已经运行了一段时间了，比如说上午10:00的时候需要暂停训练，到了下午14:00的时候想要继续训练，应该怎么操作？我发现终止train.py运行后，再重新运行train.py的时候，,没有接着中断的地方开始训练，所以我应该怎么做才能从中断的地方接着训练？我试着修改config.py,把__C.YOLO.ORIGINAL_WEIGHT = "./checkpoint/yolov3_coco.ckpt"改成自己的，比如改成__C.YOLO.ORIGINAL_WEIGHT = "./checkpoint/yolov3_test_loss=16.4555.ckpt"，但是再运行train.py的时候，输出的信息没变，依然是：

=> Restoring weights from: ./checkpoint/yolov3_coco_demo.ckpt ... 
  0%|          | 0/4014 [00:00<?, ?it/s]2020-01-11 15:11:44.567005: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.73GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-01-11 15:11:45.392378: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-01-11 15:11:45.420473: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
train loss: 4195.52:   0%|          | 1/4014 [00:07<8:15:17,  7.41s/it]2020-01-11 15:11:46.625862: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-01-11 15:11:46.704219: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
train loss: 1974.33:   0%|          | 2/4014 [00:08<6:09:40,  5.53s/it]2020-01-11 15:11:47.816559: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-01-11 15:11:47.841658: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
train loss: 3769.86:   0%|          | 3/4014 [00:09<4:44:16,  4.25s/it]2020-01-11 15:11:49.013124: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-01-11 15:11:49.089715: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
train loss: 1902.35:   0%|          | 4/4014 [00:10<3:41:04,  3.31s/it]2020-01-11 15:11:50.022054: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
train loss: 13.42: 100%|██████████| 4014/4014 [11:29<00:00,  5.82it/s]
=> Epoch:  1 Time: 2020-01-11 15:23:59 Train loss: 546.57 Test loss: 30.60 Saving ./checkpoint/yolov3_test_loss=30.6046.ckpt ...

说明还是从头开始训练了，所以怎么样才能接着训练，不从头开始？从checkpoint文件夹来看，第一次训练生成的文件是：

yolov3_test_loss=30.1690.ckpt-1.data-00000-of-00001
yolov3_test_loss=30.1690.ckpt-1.index
yolov3_test_loss=30.1690.ckpt-1.meta

而第二次生成的文件是：

yolov3_test_loss=30.6046.ckpt-1.data-00000-of-00001
yolov3_test_loss=30.6046.ckpt-1.index
yolov3_test_loss=30.6046.ckpt-1.meta

这个ckpt-1出现了2次，上面的是第一次生成的，下面的是第二次生成的。只是每次的loss不一样，然后每组文件的时间戳不一样，所请请问如何接着训练？

opened by dapsjj 10

Anyone has trained darknet53 on coco 2017?

It’s a nice work but my trained darknet53 on coco trainval2017 is always not good(about 0.33 for AP50). Can anyone give me some advice? REALLY much thanks

opened by tabsun 9
tuple index out of range

File "train.py", line 44, in loss = model.compute_loss(y_pred, y_true) File ".../tensorflow-yolov3/core/yolov3.py", line 269, in compute_loss loss_class += result[3] IndexError: tuple index out of range

opened by jiaweichen168 7
IndexError: index A is out of bounds for axis k with size B (B

https://github.com/YunYang1994/tensorflow-yolov3/issues/223#issuecomment-508348173

For those met similar problem, while the solution provided by the author is useless.
help wanted

opened by jiarenyf 6

data_aug resulting index outside the box

data_aug in parse_annotation dataset.py resulting index value outside the array of label in preprocess_true_boxes (dataset.py). So, it is suffered with error (IndexError: index xx is out of bounds for axis 0 with size yy),(xx could be negative?) during training process. please advise how to handle the problem ?

opened by ramdhan1989 5
多GPU训练的问题

我将train.py修改成了多GPU训练模式，可以正常训练得到ckpt文件，但是运行freeze_graph.py将保存的ckpt文件转成pb文件的时候，却提示错误 NotFoundError (see above for traceback): Key conv52/batch_normalization/beta not found in checkpoint 请问会是什么原因？

opened by zengqi0730 5
路径正确，但总是无法导入 yolov3_coco_demo.ckpt （解决了，自己的错误，代码正确）

我在Pycharm 从 readme 的 3.1 Train VOC dataset 开始做（其他未改动）。在 (2) train from COCO weights(recommend): 里边做到 python train.py 时，总也导入不了 yolov3_coco_demo.ckpt. 总是说 => ./checkpoint/yolov3_coco_demo.ckpt does not exist !!! => Now it starts to train YOLOV3 from scratch ...

在 config.py 里的设置也没错 __C.TRAIN.INITIAL_WEIGHT = "./checkpoint/yolov3_coco_demo.ckpt"

请问有人遇到过这样的问题么？

opened by FangliangBai 5
GPU memory out when training with BATCH_SIZE=8 in SECOND_STAGE

I used BATCH_SIZE=8 in FISRT_STAGE_EPOCHS for training,there is no problem,but when I used BATCH_SIZE=8 in SECOND_STAGE_EPOCHS for training,the error message is memory out. My GPU is RTX 2070,where is wrong?

opened by dapsjj 4
test loss降到4左右，但是mAP为0.00

我只训练了一个类别cow，从README里提供的voc数据集进行筛选，只留下只含cow的图片，最终是165张和39张。 train_from_coco，batch_size=4，训练了27轮，first_stage20轮，second_stage7轮，最终test loss降到4左右，但是运行evaluate.py时和真实情况相差较大，而且mAP也一直是0. 00。请问这是我样本太少的原因吗？还是test loss得是0.几才行？

opened by myrzx 4
nan at all epochs

Hi guys. I have nan at all epochs, what is problem? I add my class to coco.names and my train.txt looks like: /media/spectra/GMS/PDRSV2/nomeroff-net/datasets/train/X119BM199.jpg,364,422,136,451,81

opened by Spectra456 4
Bump pillow from 5.3.0 to 9.3.0 in /docs
Bumps pillow from 5.3.0 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

dependencies
opened by dependabot[bot] 0
Bump tensorflow-gpu from 1.11.0 to 2.9.3 in /docs
Bumps tensorflow-gpu from 1.11.0 to 2.9.3.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.9.3

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

TensorFlow 2.9.2

Release 2.9.2

This releases introduces several vulnerability fixes:

Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)

Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)

Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)

Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)

Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)

Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)

Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)

Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)

Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)

Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)

Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)

Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)

Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.8.4

This release introduces several vulnerability fixes:

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

... (truncated)

Commits

a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2

258f9a1 Update py_func.cc

cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474

3e75385 Update version numbers to 2.9.3

bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695

3506c90 Update RELEASE.md

8dcb48e Update RELEASE.md

4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...

6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple

5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

dependencies
opened by dependabot[bot] 0
Bump numpy from 1.15.1 to 1.22.0 in /docs
Bumps numpy from 1.15.1 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

dependencies
opened by dependabot[bot] 0

Releases(v1.0)

v1.0(Dec 31, 2018)
This release adds:

Automatic download of COCO weights and dataset.

Training pipeline on MS-COCO dataset

Evaluations on MS-COCO dataset

FPS is higher than others

Thanks to everyone who made this possible with fixes and pull requests.
Source code(tar.gz)
Source code(zip)
yolov3_coco.tar.gz(441.80 MB)

Owner

深度学习小学生

GitHub Repository https://yunyang1994.gitee.io/2018/12/28/YOLOv3-算法的一点理解/

Neural Fixed-Point Acceleration for Convex Optimization

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

27 Oct 06, 2022

Image segmentation with private İstanbul Dataset

Image Segmentation This repo was created for academic research and test result. Repo will update after academic article online. This repo contains wei

9 Dec 11, 2022

Clockwork Convnets for Video Semantic Segmentation

Clockwork Convnets for Video Semantic Segmentation This is the reference implementation of arxiv:1608.03609: Clockwork Convnets for Video Semantic Seg

141 Nov 21, 2022

Hippocampal segmentation using the UNet network for each axis

Hipposeg Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg Red: False Positive Gr

0 Sep 02, 2021

[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS).

A Strong Single-Stage Baseline for Long-Tailed Problems This project provides a strong single-stage baseline for Long-Tailed Classification (under Ima

514 Dec 23, 2022

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection arXi

59 Nov 29, 2022

N-Person-Check-Checker-Splitter - A calculator app use to divide checks

N-Person-Check-Checker-Splitter This is my from-scratch programmed calculator ap

2 Feb 15, 2022

GNN-based Recommendation Benchmark

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

73 Oct 17, 2022

SVG Icon processing tool for C++

BAWR This is a tool to automate the icons generation from sets of svg files into fonts and atlases. The main purpose of this tool is to add it to the

66 Dec 14, 2022

TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce

8 Sep 02, 2022

🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

Related tags

Overview

🆕 Are you looking for a new YOLOv3 implemented by TF2.0 ?

part 1. Quick start

part 2. Train your own dataset

2.1 Train on VOC dataset

(1) train from scratch:

(2) train from COCO weights(recommend):

2.2 Evaluate on VOC dataset

part 3. Other Implementations

Comments

9.3.0

Changes

9.3.0 (2022-10-29)

TensorFlow 2.9.3

Release 2.9.3

TensorFlow 2.9.2

Release 2.9.2

Release 2.9.3

Release 2.8.4

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Releases(v1.0)

v1.0(Dec 31, 2018)

Owner

Neural Fixed-Point Acceleration for Convex Optimization

Image segmentation with private İstanbul Dataset

Clockwork Convnets for Video Semantic Segmentation

Hippocampal segmentation using the UNet network for each axis

[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS).

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

N-Person-Check-Checker-Splitter - A calculator app use to divide checks

GNN-based Recommendation Benchmark

SVG Icon processing tool for C++

TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

EPSANet：An Efficient Pyramid Split Attention Block on Convolutional Neural Network

Relative Positional Encoding for Transformers with Linear Complexity

Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

Ascend your Jupyter Notebook usage

Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

Baseline for the Spoofing-aware Speaker Verification Challenge 2022

RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

Cascading Feature Extraction for Fast Point Cloud Registration (BMVC 2021)

A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio