YOLOv3的最小化PyTorch实现

YOLOv3的最小化PyTorch实现

PyTorch-YOLOv3

A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.

Installation

Clone and install requirements

$ git clone https://github.com/eriklindernoren/PyTorch-YOLOv3$ cd PyTorch-YOLOv3/$ sudo pip3 install -r requirements.txt

Download pretrained weights

$ cd weights/$ bash download_weights.sh

Download COCO

$ cd data/$ bash get_coco_dataset.sh

Test

Evaluates the model on COCO test.

$ python3 test.py --weights_path weights/yolov3.weights

Inference

Uses pretrained weights to make predictions on images. Below table displays the inference times when using as inputs images scaled to 256x256. The ResNet backbone measurements are taken from the YOLOv3 paper. The Darknet-53 measurement marked shows the inference time of this implementation on my 1080ti card.

$ python3 detect.py --image_folder data/samples/

Train

$ train.py [-h] [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--gradient_accumulations GRADIENT_ACCUMULATIONS] [--model_def MODEL_DEF] [--data_config DATA_CONFIG] [--pretrained_weights PRETRAINED_WEIGHTS] [--n_cpu N_CPU] [--img_size IMG_SIZE] [--checkpoint_interval CHECKPOINT_INTERVAL] [--evaluation_interval EVALUATION_INTERVAL] [--compute_map COMPUTE_MAP] [--multiscale_training MULTISCALE_TRAINING]

Example (COCO)

To train on COCO using a Darknet-53 backend pretrained on ImageNet run:

$ python3 train.py --data_config config/coco.data --pretrained_weights weights/darknet53.conv.74

Training log

---- [Epoch 7/100, Batch 7300/14658] ----+------------+--------------+--------------+--------------+| Metrics | YOLO Layer 0 | YOLO Layer 1 | YOLO Layer 2 |+------------+--------------+--------------+--------------+| grid_size | 16 | 32 | 64 || loss | 1.554926 | 1.446884 | 1.427585 || x | 0.028157 | 0.044483 | 0.051159 || y | 0.040524 | 0.035687 | 0.046307 || w | 0.078980 | 0.066310 | 0.027984 || h | 0.133414 | 0.094540 | 0.037121 || conf | 1.234448 | 1.165665 | 1.223495 || cls | 0.039402 | 0.040198 | 0.041520 || cls_acc | 44.44% | 43.59% | 32.50% || recall50 | 0.361111 | 0.384615 | 0.300000 || recall75 | 0.222222 | 0.282051 | 0.300000 || precision | 0.520000 | 0.300000 | 0.070175 || conf_obj | 0.599058 | 0.622685 | 0.651472 || conf_noobj | 0.003778 | 0.004039 | 0.004044 |+------------+--------------+--------------+--------------+Total Loss 4.429395---- ETA 0:35:48.821929

Tensorboard

Track training progress in Tensorboard:

Initialize trainingRun the command belowGo to http://localhost:6006/

$ tensorboard --logdir='logs' --port=6006

Train on Custom Dataset

Custom model

Run the commands below to create a custom model definition, replacing with the number of classes in your dataset.

$ cd config/ # Navigate to config dir$ bash create_custom_model.sh # Will create custom model 'yolov3-custom.cfg'

Classes

Add class names to data/custom/classes.names. This file should have one row per class name.

Image Folder

Move the images of your dataset to data/custom/images/.

Annotation Folder

Move your annotations to data/custom/labels/. The dataloader expects that the annotation file corresponding to the image data/custom/images/train.jpg has the path data/custom/labels/train.txt. Each row in the annotation file should define one bounding box, using the syntax label_idx x_center y_center width height. The coordinates should be scaled [0, 1], and the label_idx should be zero-indexed and correspond to the row number of the class name in data/custom/classes.names.

Define Train and Validation Sets

In data/custom/train.txt and data/custom/valid.txt, add paths to images that will be used as train and validation data respectively.

Train

To train on the custom dataset run:

$ python3 train.py --model_def config/yolov3-custom.cfg --data_config config/custom.data

Add --pretrained_weights weights/darknet53.conv.74 to train using a backend pretrained on ImageNet.

Credit

YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

Abstract We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5 AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online at https://pjreddie.com/yolo/.

[Paper] [Project Webpage] [Authors' Implementation]

@article{yolov3, title={YOLOv3: An Incremental Improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal = {arXiv}, year={2018}}

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