yolox tutorial

Data preparation

To download the dataset, please refer to prepare_data.md.

Yolox support both coco format and PAI-Itag detection format,

COCO format

To use coco data to train detection, you can refer to configs/detection/yolox/yolox_s_8xb16_300e_coco.py for more configuration details.

PAI-Itag detection format

To use pai-itag detection format data to train detection, you can refer to configs/detection/yolox/yolox_s_8xb16_300e_coco_pai.py for more configuration details.

Local & PAI-DSW

To use COCO format data, use config file configs/detection/yolox/yolox_s_8xb16_300e_coco.py

To use PAI-Itag format data, use config file configs/detection/yolox/yolox_s_8xb16_300e_coco_pai.py

Train

Single gpu:

python tools/train.py \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \
		${NUM_GPUS} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR}

Arguments

• NUM_GPUS: number of gpus

• CONFIG_PATH: the config file path of a detection method

• WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

GPUS=8
bash tools/dist_train.sh configs/detection/yolox/yolox_s_8xb16_300e_coco.py $GPUS

Evaluation

Single gpu:

python tools/eval.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		--eval

Multi gpus:

bash tools/dist_test.sh \
		${CONFIG_PATH} \
		${NUM_GPUS} \
		${CHECKPOINT} \
		--eval

Arguments

• CONFIG_PATH: the config file path of a detection method

• NUM_GPUS: number of gpus

• CHECKPOINT: the checkpoint file named as epoch_*.pth.

Examples:

GPUS=8
bash tools/dist_test.sh configs/detection/yolox/yolox_s_8xb16_300e_coco.py $GPUS work_dirs/detection/yolox/epoch_300.pth --eval

Export model

python tools/export.py \
		${CONFIG_PATH} \
		${CHECKPOINT} \
		${EXPORT_PATH}

Arguments

• CONFIG_PATH: the config file path of a detection method

• CHECKPOINT:your checkpoint file of a detection method named as epoch_*.pth.

• EXPORT_PATH: your path to save export model

Examples:

python tools/export.py configs/detection/yolox/yolox_s_8xb16_300e_coco.py \
        work_dirs/detection/yolox/epoch_300.pth \
        work_dirs/detection/yolox/epoch_300_export.pth

Inference

Download test_image

import cv2
from easycv.predictors import TorchYoloXPredictor

output_ckpt = 'work_dirs/detection/yolox/epoch_300.pth'
detector = TorchYoloXPredictor(output_ckpt)

img = cv2.imread('000000017627.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
output = detector.predict([img])
print(output)

# visualize image
from matplotlib import pyplot as plt
image = img.copy()
for box, cls_name in zip(output[0]['detection_boxes'], output[0]['detection_class_names']):
    # box is [x1,y1,x2,y2]
    box = [int(b) for b in box]
    image = cv2.rectangle(image, tuple(box[:2]), tuple(box[2:4]), (0,255,0), 2)
    cv2.putText(image, cls_name, (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 2)
plt.imshow(image)
plt.show()