YOLOv4 optimizes the speed and accuracy of object detection. It is two times faster than EfficientDet. It improves YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the COCO 2017 dataset and FPS of 41.7 on Tesla 100.
| Model | Download | Download (with sample test data) | ONNX version | Opset version | Accuracy |
|---|---|---|---|---|---|
| YOLOv4 | 251 MB | 236 MB | 1.6 | 11 | mAP of 0.5733 |
Tensorflow YOLOv4 => ONNX YOLOv4
A tutorial for the conversion process can be found in the conversion notebook.
Validation of the converted model and a graph representation of it can be found in the validation notebook.
A tutorial for running inference using onnxruntime can be found in the inference notebook.
This model expects input shapes of (1, 416, 416, 3). Each dimension represents the following variables: (batch_size, height, width, channels).
The following code shows how preprocessing is done. For more information and an example on how preprocess is done, please visit the inference notebook.
import numpy as np
import cv2
# this function is from tensorflow-yolov4-tflite/core/utils.py
def image_preprocess(image, target_size, gt_boxes=None):
ih, iw = target_size
h, w, _ = image.shape
scale = min(iw/w, ih/h)
nw, nh = int(scale * w), int(scale * h)
image_resized = cv2.resize(image, (nw, nh))
image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
dw, dh = (iw - nw) // 2, (ih-nh) // 2
image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized
image_padded = image_padded / 255.
if gt_boxes is None:
return image_padded
else:
gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw
gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh
return image_padded, gt_boxes
# input
input_size = 416
original_image = cv2.imread("input.jpg")
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)Output shape: (1, 52, 52, 3, 85)
There are 3 output layers. For each layer, there are 255 outputs: 85 values per anchor, times 3 anchors.
The 85 values of each anchor consists of 4 box coordinates describing the predicted bounding box (x, y, h, w), 1 object confidence, and 80 class confidences. Here is the class list.
The following postprocessing steps are modified from the hunglc007/tensorflow-yolov4-tflite repository.
from scipy import special
import colorsys
import random
def get_anchors(anchors_path, tiny=False):
'''loads the anchors from a file'''
with open(anchors_path) as f:
anchors = f.readline()
anchors = np.array(anchors.split(','), dtype=np.float32)
return anchors.reshape(3, 3, 2)
def postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE=[1,1,1]):
'''define anchor boxes'''
for i, pred in enumerate(pred_bbox):
conv_shape = pred.shape
output_size = conv_shape[1]
conv_raw_dxdy = pred[:, :, :, :, 0:2]
conv_raw_dwdh = pred[:, :, :, :, 2:4]
xy_grid = np.meshgrid(np.arange(output_size), np.arange(output_size))
xy_grid = np.expand_dims(np.stack(xy_grid, axis=-1), axis=2)
xy_grid = np.tile(np.expand_dims(xy_grid, axis=0), [1, 1, 1, 3, 1])
xy_grid = xy_grid.astype(np.float)
pred_xy = ((special.expit(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * STRIDES[i]
pred_wh = (np.exp(conv_raw_dwdh) * ANCHORS[i])
pred[:, :, :, :, 0:4] = np.concatenate([pred_xy, pred_wh], axis=-1)
pred_bbox = [np.reshape(x, (-1, np.shape(x)[-1])) for x in pred_bbox]
pred_bbox = np.concatenate(pred_bbox, axis=0)
return pred_bbox
def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold):
'''remove boundary boxs with a low detection probability'''
valid_scale=[0, np.inf]
pred_bbox = np.array(pred_bbox)
pred_xywh = pred_bbox[:, 0:4]
pred_conf = pred_bbox[:, 4]
pred_prob = pred_bbox[:, 5:]
# (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax)
pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5,
pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1)
# (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org)
org_h, org_w = org_img_shape
resize_ratio = min(input_size / org_w, input_size / org_h)
dw = (input_size - resize_ratio * org_w) / 2
dh = (input_size - resize_ratio * org_h) / 2
pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio
pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio
# (3) clip some boxes that are out of range
pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]),
np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1)
invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3]))
pred_coor[invalid_mask] = 0
# (4) discard some invalid boxes
bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1))
scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1]))
# (5) discard some boxes with low scores
classes = np.argmax(pred_prob, axis=-1)
scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]
score_mask = scores > score_threshold
mask = np.logical_and(scale_mask, score_mask)
coors, scores, classes = pred_coor[mask], scores[mask], classes[mask]
return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1)
def bboxes_iou(boxes1, boxes2):
'''calculate the Intersection Over Union value'''
boxes1 = np.array(boxes1)
boxes2 = np.array(boxes2)
boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
left_up = np.maximum(boxes1[..., :2], boxes2[..., :2])
right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
inter_section = np.maximum(right_down - left_up, 0.0)
inter_area = inter_section[..., 0] * inter_section[..., 1]
union_area = boxes1_area + boxes2_area - inter_area
ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)
return ious
def nms(bboxes, iou_threshold, sigma=0.3, method='nms'):
"""
:param bboxes: (xmin, ymin, xmax, ymax, score, class)
Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf
https://github.com/bharatsingh430/soft-nms
"""
classes_in_img = list(set(bboxes[:, 5]))
best_bboxes = []
for cls in classes_in_img:
cls_mask = (bboxes[:, 5] == cls)
cls_bboxes = bboxes[cls_mask]
while len(cls_bboxes) > 0:
max_ind = np.argmax(cls_bboxes[:, 4])
best_bbox = cls_bboxes[max_ind]
best_bboxes.append(best_bbox)
cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]])
iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4])
weight = np.ones((len(iou),), dtype=np.float32)
assert method in ['nms', 'soft-nms']
if method == 'nms':
iou_mask = iou > iou_threshold
weight[iou_mask] = 0.0
if method == 'soft-nms':
weight = np.exp(-(1.0 * iou ** 2 / sigma))
cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight
score_mask = cls_bboxes[:, 4] > 0.
cls_bboxes = cls_bboxes[score_mask]
return best_bboxes
def read_class_names(class_file_name):
'''loads class name from a file'''
names = {}
with open(class_file_name, 'r') as data:
for ID, name in enumerate(data):
names[ID] = name.strip('\n')
return names
def draw_bbox(image, bboxes, classes=read_class_names("coco.names"), show_label=True):
"""
bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
"""
num_classes = len(classes)
image_h, image_w, _ = image.shape
hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
random.seed(0)
random.shuffle(colors)
random.seed(None)
for i, bbox in enumerate(bboxes):
coor = np.array(bbox[:4], dtype=np.int32)
fontScale = 0.5
score = bbox[4]
class_ind = int(bbox[5])
bbox_color = colors[class_ind]
bbox_thick = int(0.6 * (image_h + image_w) / 600)
c1, c2 = (coor[0], coor[1]), (coor[2], coor[3])
cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
if show_label:
bbox_mess = '%s: %.2f' % (classes[class_ind], score)
t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0]
cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1)
cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA)
return imagePretrained yolov4 weights can be downloaded here.
mAP50 on COCO 2017 dataset is 0.5733, based on the original tensorflow model.
- YOLOv4: Optimal Speed and Accuracy of Object Detection. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao.
- Original models from Darknet Github repository.
This model is directly converted from hunglc007/tensorflow-yolov4-tflite.
MIT License