yolov4 0.15.0

No project description provided

tensorflow-yolov4

python3 -m pip install yolov4

YOLOv4 Implemented in Tensorflow 2.

Download yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Dependencies

python3 -m pip install -U pip setuptools wheel

python3 -m pip install numpy

Install OpenCV (cv2)

Tensorflow 2

python3 -m pip install tensorflow

TFlite

Ref: https://www.tensorflow.org/lite/guide/python

Objective

• Train and predict using TensorFlow 2 only
• Run yolov4 on Coral board(TPU).

Performance

Help

>>> from yolov4.tf import YOLOv4
>>> help(YOLOv4)

Inference

tensorflow

from yolov4.tf import YOLOv4

yolo = YOLOv4()

yolo.classes = "coco.names"

yolo.make_model()
yolo.load_weights("yolov4.weights", weights_type="yolo")

yolo.inference(media_path="kite.jpg")

yolo.inference(media_path="road.mp4", is_image=False)

Object detection test jupyter notebook

from yolov4.tf import YOLOv4

yolo = YOLOv4()

yolo.classes = "coco.names"
yolo.anchors = [[[23, 27], [37, 58], [81, 82]], [[81, 82], [135, 169], [344, 319]]]
yolo.xyscales = [1.05, 1.05]

yolo.make_model(tiny=True)
yolo.load_weights("yolov4-tiny.weights", weights_type="yolo", tiny=True)

yolo.inference(media_path="kite.jpg")

tensorflow lite

from yolov4.tf import YOLOv4

yolo = YOLOv4()

yolo.classes = "coco.names"

yolo.make_model()
yolo.load_weights("yolov4.weights", weights_type="yolo")

yolo.save_as_tflite("yolov4.tflite")

from yolov4.tflite import YOLOv4

yolo = YOLOv4()

yolo.classes = "coco.names"

yolo.load_tflite("yolov4.tflite")

yolo.inference("kite.jpg")

Training

import tensorflow.keras import optimizers
from yolov4.tf import YOLOv4

yolo = YOLOv4()

yolo.classes = "coco.names"
yolo.input_size = 608
yolo.batch_size = 32
yolo.subdivision = 16

yolo.make_model()
yolo.load_weights("yolov4.conv.137", weights_type="yolo")

data_set = yolo.load_dataset("val2017.txt")
# data_set = yolo.load_dataset(
#     "/home/hhk7734/darknet/data/train.txt",
#     dataset_type="yolo",
# )

optimizer = optimizers.Adam(learning_rate=1e-4)
yolo.compile(optimizer=optimizer, loss_iou_type="ciou")

yolo.fit(data_set, epochs=1500)
yolo.model.save_weights("checkpoints")

Custom training on Colab jupyter notebook

tensorflow-yolov4 (0.15.0) unstable; urgency=medium

• tf: add quantization and data_set args to save_as_tflite
• utility: media: add string length check
• model: add activation args
• model: head: remove for loop
• model: backbone: implement CSPDarknet53Tiny
• model: neck: implement PANetTiny
• model: head: implement YOLOv3HeadTiny
• model: yolov4: implement YOLOv4Tiny
• tf: weights: implement tiny funcs
• tf: reflect YOLOv4Tiny

-- Hyeonki Hong [e-mail] Wed, 15 Jul 2020 03:43:03 +0900

tensorflow-yolov4 (0.14.0) unstable; urgency=medium

• github: add python publish action
• model: neck: use bilinear in UpSampling2D
• test: update script
• tflite: refactor YOLOv4
• utility: predict: fix according to pylint warning
• pylint: update .pylintrc
• model: head: use tf.constant to avoid broadcasting
• github: add python lint action

-- Hyeonki Hong [e-mail] Tue, 14 Jul 2020 02:14:12 +0900

tensorflow-yolov4 (0.13.0) unstable; urgency=medium

• tf: remove tensorboard callback
• tf: modify compile() and fit() to be similar to model
• yolov4: rename 'data' to 'test'
• utility: media: update docs and variable name
• utility: predict: remove batch_size
• utility: media: rename funcs
• tf: add utility funcs to YOLOv4 member funcs
• test: add test script

-- Hyeonki Hong [e-mail] Mon, 13 Jul 2020 13:36:32 +0900

tensorflow-yolov4 (0.12.0) unstable; urgency=medium

• utility: train: refactor bbox_*iou and remove duplicate funcs
• yolov4: rename parameters
• yolov4: remove utils
• tf: add FileNotFoundError in YOLOv4.inference()
• utility: predict: add dimension for batch size
• pylint: update .pylintrc
• tf: add YOLOv4.save_as_tflite()
• model: clean up
• utility: weights: move to tf.weights
• utility: train: move to tf.train
• model: neck: implement PANet
• mdel: head: implement YOLOv3Head
• model: yolov4: Apply neck and head class
• yolov4: reflect model changes
• tf: dataset: add batch_size
• tf: train: implement YOLOv4Loss
• utility: media: modify rectangle thickness
• model: common: use softplus instead of ln(1+exp(x))
• tf: train: use epsilon instead of tf.math.divide_no_nan
• tf: refactor YOLOv4.fit() and .compile()

-- Hyeonki Hong [e-mail] Sun, 12 Jul 2020 03:50:50 +0900

tensorflow-yolov4 (0.11.0) unstable; urgency=medium

• tf: remove utils.draw_bbox in predict()
• yolov4: rename files and functions and change order
• utility: utils: remove get_anchors()
• utility: media: impelment resize(), draw_bbox()
• utility: utils: implement DIoU_NMS
• utility: utils: fix dimensional calculation problems
• utility: refactor dataset
• tf: remove train
• utility: train: implement make_compiled_loss()
• utility: media: fix bug that could not resize some images
• utility: train: remove problem of division by zero

-- Hyeonki Hong [e-mail] Mon, 29 Jun 2020 21:05:39 +0900

tensorflow-yolov4 (0.10.0) unstable; urgency=medium

• core: yolov4: refactor decode()
• core: utils: remove sigmoid in postprocess_bbboxe()
• tf: apply YOLOv4 changes to make_model()
• core: yolov4: move decode_train() to tf.YOLOv4.train()

-- Hyeonki Hong [e-mail] Thu, 25 Jun 2020 00:48:44 +0900

tensorflow-yolov4 (0.9.0) unstable; urgency=medium

• tf: modify hyperparameters as properties
• tf: add weights_type argument to load_weights()
• core: utils: implement _np_fromfile()
• core: utils: implement a way to partially load weights
• tf: train: move learning_rate_* to argument
• core: move YOLOConv2D to common
• core: common: remove bn argument of YOLOConv2D
• core: utils: refactor yolo_conv2d_set_weights
• core: yolov4: refactor YOLOv4
• core: utils: refactor load_weights
• tf: refactor make_model
• yolov4: change YoloV4 to YOLOv4

-- Hyeonki Hong [e-mail] Wed, 24 Jun 2020 02:58:27 +0900

tensorflow-yolov4 (0.8.0) unstable; urgency=medium

• core: use tf.keras.layers.UpSampling2D
• core: refactor Mish
• core: common: remove residual_block
• core: remove sequential in _ResBlock
• core: backbone: Set LeakyReLU's alpha to 0.1

-- Hyeonki Hong [e-mail] Tue, 23 Jun 2020 02:21:01 +0900

tensorflow-yolov4 (0.7.0) unstable; urgency=medium

• tf: fix to proceed to the next step even if an error occurs
• tf: modify video_interval_ms to cv_waitKey_delay
• core: backbone: refactor CSPDarknet53
• core: utils: implement csp_darknet53_set_weights()

-- Hyeonki Hong [e-mail] Mon, 22 Jun 2020 23:01:32 +0900

tensorflow-yolov4 (0.6.0) unstable; urgency=medium

• tf: set first_step_epochs according to the weight usage
• tf: fix syntax error

-- Hyeonki Hong [e-mail] Fri, 19 Jun 2020 17:09:57 +0900

tensorflow-yolov4 (0.5.0) unstable; urgency=medium

• core: dataset: add yolo type
• tf: add dataset_type parameter to YoloV4.train
• tf: add epochs parameter to train
• tf: add save_interval parameter to train

-- Hyeonki Hong [e-mail] Fri, 19 Jun 2020 14:30:50 +0900

tensorflow-yolov4 (0.4.0) unstable; urgency=medium

• core: dataset: remove cfg module
• tf: implement YoloV4.train

-- Hyeonki Hong [e-mail] Thu, 11 Jun 2020 17:45:44 +0900

tensorflow-yolov4 (0.3.0) unstable; urgency=medium

• core: utils: use numpy instead of tensorflow
• pypi: remove install_requires and change to manual installation
• yolov4: add video_interval_ms

-- Hyeonki Hong [e-mail] Mon, 08 Jun 2020 23:59:41 +0900

tensorflow-yolov4 (0.2.0) unstable; urgency=medium

• pylint: create .pylintrc and run black
• core: remove config.py
• yolov4: change tfyolov4 to yolov4
• yolov4: remove detect**.py and implement YoloV4.inference

-- Hyeonki Hong [e-mail] Mon, 08 Jun 2020 02:20:49 +0900

tensorflow-yolov4 (0.1.0) unstable; urgency=medium

• yolov4: fork from 'hunglc007/tensorflow-yolov4-tflite'

-- Hyeonki Hong [e-mail] Fri, 05 Jun 2020 20:17:45 +0900