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

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

  1. Clone this file
$ git clone https://github.com/YunYang1994/tensorflow-yolov3.git
  1. You are supposed to install some dependencies before getting out hands with these codes.
$ cd tensorflow-yolov3
$ pip install -r ./docs/requirements.txt
  1. 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
  1. 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:

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
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

    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?

    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?

    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

    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
  • data_aug resulting index outside the box

    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

    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)

    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

    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

    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

    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

    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

    ... (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)

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  • Bump tensorflow-gpu from 1.11.0 to 2.9.3 in /docs

    Bump tensorflow-gpu from 1.11.0 to 2.9.3 in /docs

    Bumps tensorflow-gpu from 1.11.0 to 2.9.3.

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    TensorFlow 2.9.3

    Release 2.9.3

    This release introduces several vulnerability fixes:

    TensorFlow 2.9.2

    Release 2.9.2

    This releases introduces several vulnerability fixes:

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    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

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    • 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
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  • Bump numpy from 1.15.1 to 1.22.0 in /docs

    Bump numpy from 1.15.1 to 1.22.0 in /docs

    Bumps numpy from 1.15.1 to 1.22.0.

    Release notes

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    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.
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    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.

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    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)

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