This model is a real-time neural network for object instance segmentation that detects 80 different classes.
| Model | Download | Download (with sample test data) | ONNX version | Opset version | Accuracy |
|---|---|---|---|---|---|
| Mask R-CNN R-50-FPN | 177.9 MB | 168.8 MB | 1.5 | 10 | mAP of 0.36 & 0.33 |
| Mask R-CNN R-50-FPN-fp32 | 169.7 MB | 157.3 MB | 1.9 | 12 | mAP of 0.3372 |
| Mask R-CNN R-50-FPN-int8 | 44 MB | 38 MB | 1.9 | 12 | mAP of 0.3314 |
| Mask R-CNN R-50-FPN-qdq | 44 MB | 30 MB | 1.9 | 12 | mAP of 0.3328 |
Compared with the Mask R-CNN R-50-FPN-fp32, Mask R-CNN R-50-FPN-int8's mAP decline is 0.0058 and performance improvement is 1.99x.
Note the performance depends on the test hardware.
Performance data here is collected with Intel® Xeon® Platinum 8280 Processor, 1s 4c per instance, CentOS Linux 8.3, data batch size is 1.
Image shape (3x'height'x'width')
The images have to be loaded in to a range of [0, 255], resized, converted to BGR and then normalized using mean = [102.9801, 115.9465, 122.7717]. The transformation should preferably happen at preprocessing.
This model can take images of different sizes as input. However, to achieve best performance, it is recommended to resize the image such that both height and width are within the range of [800, 1333], and then pad the image with zeros such that both height and width are divisible by 32.
The following code shows how to preprocess the demo image:
import numpy as np
from PIL import Image
def preprocess(image):
# Resize
ratio = 800.0 / min(image.size[0], image.size[1])
image = image.resize((int(ratio * image.size[0]), int(ratio * image.size[1])), Image.BILINEAR)
# Convert to BGR
image = np.array(image)[:, :, [2, 1, 0]].astype('float32')
# HWC -> CHW
image = np.transpose(image, [2, 0, 1])
# Normalize
mean_vec = np.array([102.9801, 115.9465, 122.7717])
for i in range(image.shape[0]):
image[i, :, :] = image[i, :, :] - mean_vec[i]
# Pad to be divisible of 32
import math
padded_h = int(math.ceil(image.shape[1] / 32) * 32)
padded_w = int(math.ceil(image.shape[2] / 32) * 32)
padded_image = np.zeros((3, padded_h, padded_w), dtype=np.float32)
padded_image[:, :image.shape[1], :image.shape[2]] = image
image = padded_image
return image
img = Image.open('dependencies/demo.jpg')
img_data = preprocess(img)The model has 4 outputs.
boxes: ('nbox'x4), in (xmin, ymin, xmax, ymax).
labels: ('nbox').
scores: ('nbox').
masks: ('nbox', 1, 28, 28).
The following code shows how to patch the original image with detections, class annotations and segmentation, filtered by scores:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import pycocotools.mask as mask_util
import cv2
classes = [line.rstrip('\n') for line in open('coco_classes.txt')]
def display_objdetect_image(image, boxes, labels, scores, masks, score_threshold=0.7):
# Resize boxes
ratio = 800.0 / min(image.size[0], image.size[1])
boxes /= ratio
_, ax = plt.subplots(1, figsize=(12,9))
image = np.array(image)
for mask, box, label, score in zip(masks, boxes, labels, scores):
# Showing boxes with score > 0.7
if score <= score_threshold:
continue
# Finding contour based on mask
mask = mask[0, :, :, None]
int_box = [int(i) for i in box]
mask = cv2.resize(mask, (int_box[2]-int_box[0]+1, int_box[3]-int_box[1]+1))
mask = mask > 0.5
im_mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
x_0 = max(int_box[0], 0)
x_1 = min(int_box[2] + 1, image.shape[1])
y_0 = max(int_box[1], 0)
y_1 = min(int_box[3] + 1, image.shape[0])
mask_y_0 = max(y_0 - box[1], 0)
mask_y_1 = mask_y_0 + y_1 - y_0
mask_x_0 = max(x_0 - box[0], 0)
mask_x_1 = mask_x_0 + x_1 - x_0
im_mask[y_0:y_1, x_0:x_1] = mask[
mask_y_0 : mask_y_1, mask_x_0 : mask_x_1
]
im_mask = im_mask[:, :, None]
# OpenCV version 4.x
contours, hierarchy = cv2.findContours(
im_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
image = cv2.drawContours(image, contours, -1, 25, 3)
rect = patches.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor='b', facecolor='none')
ax.annotate(classes[label] + ':' + str(np.round(score, 2)), (box[0], box[1]), color='w', fontsize=12)
ax.add_patch(rect)
ax.imshow(image)
plt.show()
display_objdetect_image(img, boxes, labels, scores, masks)The original pretrained Mask R-CNN model is from facebookresearch/maskrcnn-benchmark, compute mAP the same as Detectron on coco_2014_minival dataset from COCO, which is exactly equivalent to the coco_2017_val dataset.
Mask R-CNN R-50-FPN: Metric is COCO mAP (averaged over IoU of 0.5:0.95), computed over 2017 COCO val data. box mAP of 0.361, and mask mAP of 0.327.
Mask R-CNN R-50-FPN-fp32 & Mask R-CNN R-50-FPN-int8: Metric is COCO box mAP@[IoU=0.50:0.95 | area=all | maxDets=100], computed over 2017 COCO val data.
Mask R-CNN R-50-FPN-int8 and Mask R-CNN R-50-FPN-qdq are obtained by quantizing Mask R-CNN R-50-FPN-fp32 model. We use Intel® Neural Compressor with onnxruntime backend to perform quantization. View the instructions to understand how to use Intel® Neural Compressor for quantization.
onnx: 1.9.0 onnxruntime: 1.10.0
wget https://github.com/onnx/models/raw/main/vision/object_detection_segmentation/mask-rcnn/model/MaskRCNN-12.onnxbash run_tuning.sh --input_model=path/to/model \ # model path as *.onnx
--config=mask_rcnn.yaml \
--data_path=path/to/COCO2017 \
--output_model=path/to/saveKaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2017.
Massa, Francisco and Girshick, Ross. maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch. facebookresearch/maskrcnn-benchmark.
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This model is converted from facebookresearch/maskrcnn-benchmark with modifications in repository.
- mengniwang95 (Intel)
- yuwenzho (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
MIT License