YOLOv3 implementation in TensorFlow 2.x
Project description
YOLOv3-TF
YOLOv3 implementation in TensorFlow 2.x
Installation
pip install yolov3-tf
Depends on tensorflow >=2.3.0 <=2.9.1
Usage
The package consists of three core modules -
- dataset
- models
- utils
Dataset
The dataset.py
module is for loading and transforming the tfrecords for object detection. The examples in the input tfrecords must match the parsing schema.
import yolov3_tf.dataset as dataset
train_dataset = dataset.load_tfrecord_dataset(tfrecords_path)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.map(
lambda x, y: (
dataset.transform_images(x, image_dim),
dataset.transform_targets(y, anchors, anchor_masks, image_dim),
)
)
Models
The models.py
module consists of implementation of two YOLOv3 and YOLOv3 tiny in Tesnsorflow.
from yolov3_tf.models import YoloV3, YoloV3Tiny
model = YoloV3(image_dim = 416, training=True, classes=10)
Utils
The utils.py
module provides some common functions for training YOLOv3 model, viz., loading weights, freezing layers, drawing boxes on images, compute iou
# convert weights
from yolov3_tf.models import YoloV3, YoloV3Tiny
from yolov3_tf import utils
yolo = YoloV3()
utils.load_darknet_weights(yolo, weights_path, is_tiny=False)
yolo.save_weights(converted_weights_path)
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