#!/usr/bin/env python from __future__ import print_function import hashlib import os import sys import tarfile import requests if sys.version_info[0] < 3: from urllib2 import urlopen else: from urllib.request import urlopen class Model: MB = 1024*1024 BUFSIZE = 10*MB def __init__(self, **kwargs): self.name = kwargs.pop('name') self.url = kwargs.pop('url', None) self.downloader = kwargs.pop('downloader', None) self.filename = kwargs.pop('filename') self.sha = kwargs.pop('sha', None) self.archive = kwargs.pop('archive', None) self.member = kwargs.pop('member', None) def __str__(self): return 'Model <{}>'.format(self.name) def printRequest(self, r): def getMB(r): d = dict(r.info()) for c in ['content-length', 'Content-Length']: if c in d: return int(d[c]) / self.MB return '' print(' {} {} [{} Mb]'.format(r.getcode(), r.msg, getMB(r))) def verify(self): if not self.sha: return False print(' expect {}'.format(self.sha)) sha = hashlib.sha1() try: with open(self.filename, 'rb') as f: while True: buf = f.read(self.BUFSIZE) if not buf: break sha.update(buf) print(' actual {}'.format(sha.hexdigest())) self.sha_actual = sha.hexdigest() return self.sha == self.sha_actual except Exception as e: print(' catch {}'.format(e)) def get(self): if self.verify(): print(' hash match - skipping') return True basedir = os.path.dirname(self.filename) if basedir and not os.path.exists(basedir): print(' creating directory: ' + basedir) os.makedirs(basedir, exist_ok=True) if self.archive or self.member: assert(self.archive and self.member) print(' hash check failed - extracting') print(' get {}'.format(self.member)) self.extract() elif self.url: print(' hash check failed - downloading') print(' get {}'.format(self.url)) self.download() else: assert self.downloader print(' hash check failed - downloading') sz = self.downloader(self.filename) print(' size = %.2f Mb' % (sz / (1024.0 * 1024))) print(' done') print(' file {}'.format(self.filename)) candidate_verify = self.verify() if not candidate_verify: self.handle_bad_download() return candidate_verify def download(self): try: r = urlopen(self.url, timeout=60) self.printRequest(r) self.save(r) except Exception as e: print(' catch {}'.format(e)) def extract(self): try: with tarfile.open(self.archive) as f: assert self.member in f.getnames() self.save(f.extractfile(self.member)) except Exception as e: print(' catch {}'.format(e)) def save(self, r): with open(self.filename, 'wb') as f: print(' progress ', end='') sys.stdout.flush() while True: buf = r.read(self.BUFSIZE) if not buf: break f.write(buf) print('>', end='') sys.stdout.flush() def handle_bad_download(self): if os.path.exists(self.filename): # rename file for further investigation try: # NB: using `self.sha_actual` may create unbounded number of files rename_target = self.filename + '.invalid' # TODO: use os.replace (Python 3.3+) try: if os.path.exists(rename_target): # avoid FileExistsError on Windows from os.rename() os.remove(rename_target) finally: os.rename(self.filename, rename_target) print(' renaming invalid file to ' + rename_target) except: import traceback traceback.print_exc() finally: if os.path.exists(self.filename): print(' deleting invalid file') os.remove(self.filename) def GDrive(gid): def download_gdrive(dst): session = requests.Session() # re-use cookies URL = "https://docs.google.com/uc?export=download" response = session.get(URL, params = { 'id' : gid }, stream = True) def get_confirm_token(response): # in case of large files for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None token = get_confirm_token(response) if token: params = { 'id' : gid, 'confirm' : token } response = session.get(URL, params = params, stream = True) BUFSIZE = 1024 * 1024 PROGRESS_SIZE = 10 * 1024 * 1024 sz = 0 progress_sz = PROGRESS_SIZE with open(dst, "wb") as f: for chunk in response.iter_content(BUFSIZE): if not chunk: continue # keep-alive f.write(chunk) sz += len(chunk) if sz >= progress_sz: progress_sz += PROGRESS_SIZE print('>', end='') sys.stdout.flush() print('') return sz return download_gdrive models = [ Model( name='GoogleNet', url='http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel', sha='405fc5acd08a3bb12de8ee5e23a96bec22f08204', filename='bvlc_googlenet.caffemodel'), Model( name='Alexnet', url='http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel', sha='9116a64c0fbe4459d18f4bb6b56d647b63920377', filename='bvlc_alexnet.caffemodel'), Model( name='Inception', url='https://github.com/petewarden/tf_ios_makefile_example/raw/master/data/tensorflow_inception_graph.pb', sha='c8a5a000ee8d8dd75886f152a50a9c5b53d726a5', filename='tensorflow_inception_graph.pb'), Model( name='Enet', # https://github.com/e-lab/ENet-training url='https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1', sha='b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315', filename='Enet-model-best.net'), Model( name='Fcn', url='http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel', sha='c449ea74dd7d83751d1357d6a8c323fcf4038962', filename='fcn8s-heavy-pascal.caffemodel'), Model( name='Ssd_vgg16', url='https://www.dropbox.com/s/8apyk3uzk2vl522/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel?dl=1', sha='0fc294d5257f3e0c8a3c5acaa1b1f6a9b0b6ade0', filename='VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel'), Model( name='ResNet50', url='https://onedrive.live.com/download?cid=4006CBB8476FF777&resid=4006CBB8476FF777%2117895&authkey=%21AAFW2%2DFVoxeVRck', sha='b7c79ccc21ad0479cddc0dd78b1d20c4d722908d', filename='ResNet-50-model.caffemodel'), Model( name='SqueezeNet_v1.1', url='https://raw.githubusercontent.com/DeepScale/SqueezeNet/b5c3f1a23713c8b3fd7b801d229f6b04c64374a5/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel', sha='3397f026368a45ae236403ccc81cfcbe8ebe1bd0', filename='squeezenet_v1.1.caffemodel'), Model( name='MobileNet-SSD', # https://github.com/chuanqi305/MobileNet-SSD url='https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc', sha='994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a', filename='MobileNetSSD_deploy.caffemodel'), Model( name='MobileNet-SSD', url='https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/daef68a6c2f5fbb8c88404266aa28180646d17e0/MobileNetSSD_deploy.prototxt', sha='d77c9cf09619470d49b82a9dd18704813a2043cd', filename='MobileNetSSD_deploy.prototxt'), Model( name='OpenFace', # https://github.com/cmusatyalab/openface url='https://storage.cmusatyalab.org/openface-models/nn4.small2.v1.t7', sha='ac8161a4376fb5a79ceec55d85bbb57ef81da9fe', filename='openface_nn4.small2.v1.t7'), Model( name='YoloV2voc', # https://pjreddie.com/darknet/yolo/ url='https://pjreddie.com/media/files/yolo-voc.weights', sha='1cc1a7f8ad12d563d85b76e9de025dc28ac397bb', filename='yolo-voc.weights'), Model( name='TinyYoloV2voc', # https://pjreddie.com/darknet/yolo/ url='https://pjreddie.com/media/files/yolov2-tiny-voc.weights', sha='24b4bd049fc4fa5f5e95f684a8967e65c625dff9', filename='tiny-yolo-voc.weights'), Model( name='DenseNet-121', # https://github.com/shicai/DenseNet-Caffe url='https://drive.google.com/uc?export=download&id=0B7ubpZO7HnlCcHlfNmJkU2VPelE', sha='02b520138e8a73c94473b05879978018fefe947b', filename='DenseNet_121.caffemodel'), Model( name='DenseNet-121', url='https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt', sha='4922099342af5993d9d09f63081c8a392f3c1cc6', filename='DenseNet_121.prototxt'), Model( name='Fast-Neural-Style', url='http://cs.stanford.edu/people/jcjohns/fast-neural-style/models/eccv16/starry_night.t7', sha='5b5e115253197b84d6c6ece1dafe6c15d7105ca6', filename='fast_neural_style_eccv16_starry_night.t7'), Model( name='Fast-Neural-Style', url='http://cs.stanford.edu/people/jcjohns/fast-neural-style/models/instance_norm/feathers.t7', sha='9838007df750d483b5b5e90b92d76e8ada5a31c0', filename='fast_neural_style_instance_norm_feathers.t7'), Model( name='MobileNet-SSD (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz', sha='a88a18cca9fe4f9e496d73b8548bfd157ad286e2', filename='ssd_mobilenet_v1_coco_11_06_217.tar.gz'), Model( name='MobileNet-SSD (TensorFlow)', archive='ssd_mobilenet_v1_coco_11_06_217.tar.gz', member='ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb', sha='aaf36f068fab10359eadea0bc68388d96cf68139', filename='ssd_mobilenet_v1_coco.pb'), Model( name='MobileNet-SSD v1 (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz', sha='6157ddb6da55db2da89dd561eceb7f944928e317', filename='ssd_mobilenet_v1_coco_2017_11_17.tar.gz'), Model( name='MobileNet-SSD v1 (TensorFlow)', archive='ssd_mobilenet_v1_coco_2017_11_17.tar.gz', member='ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb', sha='9e4bcdd98f4c6572747679e4ce570de4f03a70e2', filename='ssd_mobilenet_v1_coco_2017_11_17.pb'), Model( name='MobileNet-SSD v2 (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz', sha='69c93d29e292bc9682396a5c78355b1dfe481b61', filename='ssd_mobilenet_v2_coco_2018_03_29.tar.gz'), Model( name='MobileNet-SSD v2 (TensorFlow)', archive='ssd_mobilenet_v2_coco_2018_03_29.tar.gz', member='ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb', sha='35d571ac314f1d32ae678a857f87cc0ef6b220e8', filename='ssd_mobilenet_v2_coco_2018_03_29.pb'), Model( name='Colorization', url='https://raw.githubusercontent.com/richzhang/colorization/master/models/colorization_deploy_v2.prototxt', sha='f528334e386a69cbaaf237a7611d833bef8e5219', filename='colorization_deploy_v2.prototxt'), Model( name='Colorization', url='http://eecs.berkeley.edu/~rich.zhang/projects/2016_colorization/files/demo_v2/colorization_release_v2.caffemodel', sha='21e61293a3fa6747308171c11b6dd18a68a26e7f', filename='colorization_release_v2.caffemodel'), Model( name='Face_detector', url='https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel', sha='15aa726b4d46d9f023526d85537db81cbc8dd566', filename='opencv_face_detector.caffemodel'), Model( name='Face_detector (FP16)', url='https://github.com/opencv/opencv_3rdparty/raw/19512576c112aa2c7b6328cb0e8d589a4a90a26d/res10_300x300_ssd_iter_140000_fp16.caffemodel', sha='31fc22bfdd907567a04bb45b7cfad29966caddc1', filename='opencv_face_detector_fp16.caffemodel'), Model( name='Face_detector (UINT8)', url='https://github.com/opencv/opencv_3rdparty/raw/8033c2bc31b3256f0d461c919ecc01c2428ca03b/opencv_face_detector_uint8.pb', sha='4f2fdf6f231d759d7bbdb94353c5a68690f3d2ae', filename='opencv_face_detector_uint8.pb'), Model( name='InceptionV2-SSD (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz', sha='b9546dcd1ba99282b5bfa81c460008c885ca591b', filename='ssd_inception_v2_coco_2017_11_17.tar.gz'), Model( name='InceptionV2-SSD (TensorFlow)', archive='ssd_inception_v2_coco_2017_11_17.tar.gz', member='ssd_inception_v2_coco_2017_11_17/frozen_inference_graph.pb', sha='554a75594e9fd1ccee291b3ba3f1190b868a54c9', filename='ssd_inception_v2_coco_2017_11_17.pb'), Model( name='Faster-RCNN', # https://github.com/rbgirshick/py-faster-rcnn url='https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0', sha='51bca62727c3fe5d14b66e9331373c1e297df7d1', filename='faster_rcnn_models.tgz'), Model( name='Faster-RCNN VGG16', archive='faster_rcnn_models.tgz', member='faster_rcnn_models/VGG16_faster_rcnn_final.caffemodel', sha='dd099979468aafba21f3952718a9ceffc7e57699', filename='VGG16_faster_rcnn_final.caffemodel'), Model( name='Faster-RCNN ZF', archive='faster_rcnn_models.tgz', member='faster_rcnn_models/ZF_faster_rcnn_final.caffemodel', sha='7af886686f149622ed7a41c08b96743c9f4130f5', filename='ZF_faster_rcnn_final.caffemodel'), Model( name='R-FCN', # https://github.com/YuwenXiong/py-R-FCN url='https://onedrive.live.com/download?cid=10B28C0E28BF7B83&resid=10B28C0E28BF7B83%215317&authkey=%21AIeljruhoLuail8', sha='bb3180da68b2b71494f8d3eb8f51b2d47467da3e', filename='rfcn_models.tar.gz'), Model( name='R-FCN ResNet-50', archive='rfcn_models.tar.gz', member='rfcn_models/resnet50_rfcn_final.caffemodel', sha='e00beca7af2790801efb1724d77bddba89e7081c', filename='resnet50_rfcn_final.caffemodel'), Model( name='OpenPose/pose/coco', # https://github.com/CMU-Perceptual-Computing-Lab/openpose url='http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel', sha='ac7e97da66f3ab8169af2e601384c144e23a95c1', filename='openpose_pose_coco.caffemodel'), Model( name='OpenPose/pose/mpi', # https://github.com/CMU-Perceptual-Computing-Lab/openpose url='http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel', sha='a344f4da6b52892e44a0ca8a4c68ee605fc611cf', filename='openpose_pose_mpi.caffemodel'), Model( name='YOLOv3', # https://pjreddie.com/darknet/yolo/ url='https://pjreddie.com/media/files/yolov3.weights', sha='520878f12e97cf820529daea502acca380f1cb8e', filename='yolov3.weights'), Model( name='EAST', # https://github.com/argman/EAST (a TensorFlow model), https://arxiv.org/abs/1704.03155v2 (a paper) url='https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1', sha='3ca8233d6edd748f7ed23246c8ca24cbf696bb94', filename='frozen_east_text_detection.tar.gz'), Model( name='EAST', archive='frozen_east_text_detection.tar.gz', member='frozen_east_text_detection.pb', sha='fffabf5ac36f37bddf68e34e84b45f5c4247ed06', filename='frozen_east_text_detection.pb'), Model( name='Faster-RCNN, InveptionV2 (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz', sha='c710f25e5c6a3ce85fe793d5bf266d581ab1c230', filename='faster_rcnn_inception_v2_coco_2018_01_28.tar.gz'), Model( name='Faster-RCNN, InveptionV2 (TensorFlow)', archive='faster_rcnn_inception_v2_coco_2018_01_28.tar.gz', member='faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb', sha='f2e4bf386b9bb3e25ddfcbbd382c20f417e444f3', filename='faster_rcnn_inception_v2_coco_2018_01_28.pb'), Model( name='ssd_mobilenet_v1_ppn_coco (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz', sha='549ae0fd82c202786abe53c306b191c578599c44', filename='ssd_mobilenet_v1_ppn_coco.tar.gz'), Model( name='ssd_mobilenet_v1_ppn_coco (TensorFlow)', archive='ssd_mobilenet_v1_ppn_coco.tar.gz', member='ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03/frozen_inference_graph.pb', sha='7943c51c6305b38173797d4afbf70697cf57ab48', filename='ssd_mobilenet_v1_ppn_coco.pb'), Model( name='mask_rcnn_inception_v2_coco_2018_01_28 (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz', sha='f8a920756744d0f7ee812b3ec2474979f74ab40c', filename='mask_rcnn_inception_v2_coco_2018_01_28.tar.gz'), Model( name='mask_rcnn_inception_v2_coco_2018_01_28 (TensorFlow)', archive='mask_rcnn_inception_v2_coco_2018_01_28.tar.gz', member='mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb', sha='c8adff66a1e23e607f57cf1a7cfabad0faa371f9', filename='mask_rcnn_inception_v2_coco_2018_01_28.pb'), Model( name='faster_rcnn_resnet50_coco (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz', sha='3066e8dd156b99c4b4d78a2ccd13e33fc263beb7', filename='faster_rcnn_resnet50_coco_2018_01_28.tar.gz'), Model( name='faster_rcnn_resnet50_coco (TensorFlow)', archive='faster_rcnn_resnet50_coco_2018_01_28.tar.gz', member='faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb', sha='27feaef9924650299b2ef5d29f041627b6f298b2', filename='faster_rcnn_resnet50_coco_2018_01_28.pb'), Model( name='AlexNet (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_alexnet.tar.gz', sha='c713be2852472582224fa7395e2ab4641f8b6356', filename='bvlc_alexnet.tar.gz'), Model( name='AlexNet (ONNX)', archive='bvlc_alexnet.tar.gz', member='bvlc_alexnet/model.onnx', sha='b256703f2b125d8681a0a6e5a40a6c9deb7d2b4b', filename='onnx/models/alexnet.onnx'), Model( name='GoogleNet (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_googlenet.tar.gz', sha='739732220ba2e3efa88f7c26f13badad9b7514bc', filename='bvlc_googlenet.tar.gz'), Model( name='GoogleNet (ONNX)', archive='bvlc_googlenet.tar.gz', member='bvlc_googlenet/model.onnx', sha='534a16d7e2472f6a9a1925a5ee6c9abc2f5c02b0', filename='onnx/models/googlenet.onnx'), Model( name='CaffeNet (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_reference_caffenet.tar.gz', sha='f9f5dd60d4c9172a7e26bd4268eab7ecddb37393', filename='bvlc_reference_caffenet.tar.gz'), Model( name='CaffeNet (ONNX)', archive='bvlc_reference_caffenet.tar.gz', member='bvlc_reference_caffenet/model.onnx', sha='6b2be0cd598914e13b60787c63cba0533723d746', filename='onnx/models/caffenet.onnx'), Model( name='CaffeNet (ONNX)', archive='bvlc_reference_caffenet.tar.gz', member='bvlc_reference_caffenet/test_data_set_0/input_0.pb', sha='e5d6fb75a66ef157023a7fc2f88abdcb371f2f16', filename='onnx/data/input_caffenet.pb'), Model( name='CaffeNet (ONNX)', archive='bvlc_reference_caffenet.tar.gz', member='bvlc_reference_caffenet/test_data_set_0/output_0.pb', sha='eaff902ef71a648aaaeffa495e5fddf2dc0b77c1', filename='onnx/data/output_caffenet.pb'), Model( name='RCNN_ILSVRC13 (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_reference_rcnn_ilsvrc13.tar.gz', sha='b1b27a41066c26f824d57e99036dc885459017f0', filename='bvlc_reference_rcnn_ilsvrc13.tar.gz'), Model( name='RCNN_ILSVRC13 (ONNX)', archive='bvlc_reference_rcnn_ilsvrc13.tar.gz', member='bvlc_reference_rcnn_ilsvrc13/model.onnx', sha='fbf174b62a1918bff43c0287e41fdc6017b46256', filename='onnx/models/rcnn_ilsvrc13.onnx'), Model( name='RCNN_ILSVRC13 (ONNX)', archive='bvlc_reference_rcnn_ilsvrc13.tar.gz', member='bvlc_reference_rcnn_ilsvrc13/test_data_set_0/input_0.pb', sha='dcfd587bede888606a7f10e9feadc7f25bed7da4', filename='onnx/data/input_rcnn_ilsvrc13.pb'), Model( name='RCNN_ILSVRC13 (ONNX)', archive='bvlc_reference_rcnn_ilsvrc13.tar.gz', member='bvlc_reference_rcnn_ilsvrc13/test_data_set_0/output_0.pb', sha='e09eea540b93a2f450e32db59e198ca96c3b8637', filename='onnx/data/output_rcnn_ilsvrc13.pb'), Model( name='ZFNet512 (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/zfnet512.tar.gz', sha='c040c455c8aac71c8cda57595b698b76449e4ff4', filename='zfnet512.tar.gz'), Model( name='ZFNet512 (ONNX)', archive='zfnet512.tar.gz', member='zfnet512/model.onnx', sha='c32b9ae0bbe65e2ee60f98639b170645000e2c75', filename='onnx/models/zfnet512.onnx'), Model( name='ZFNet512 (ONNX)', archive='zfnet512.tar.gz', member='zfnet512/test_data_set_0/input_0.pb', sha='2dc2c8020edbd84a52f0550d6666c9ae7e93c01f', filename='onnx/data/input_zfnet512.pb'), Model( name='ZFNet512 (ONNX)', archive='zfnet512.tar.gz', member='zfnet512/test_data_set_0/output_0.pb', sha='a74974096088954ca4e4e89bec212c1ac2ab0745', filename='onnx/data/output_zfnet512.pb'), Model( name='VGG16_bn (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/vgg/vgg16-bn/vgg16-bn.tar.gz', sha='60f4685aed632d2ce3b137017cf44ae1a5c55459', filename='vgg16-bn.tar.gz'), Model( name='VGG16_bn (ONNX)', archive='vgg16-bn.tar.gz', member='vgg16-bn/vgg16-bn.onnx', sha='e282e2137f1317d03ca1f2702e9cfddaf847e44d', filename='onnx/models/vgg16-bn.onnx'), Model( name='VGG16_bn (ONNX)', archive='vgg16-bn.tar.gz', member='vgg16-bn/test_data_set_0/input_0.pb', sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a', filename='onnx/data/input_vgg16-bn.pb'), Model( name='VGG16_bn (ONNX)', archive='vgg16-bn.tar.gz', member='vgg16-bn/test_data_set_0/output_0.pb', sha='418b1a426a2a4105cfd9a77a965ae67dc105891b', filename='onnx/data/output_vgg16-bn.pb'), Model( name='ResNet-18v1 (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet18v1/resnet18v1.tar.gz', sha='d132be4857d024de9caa21fd5300dee7c063bc35', filename='resnet18v1.tar.gz'), Model( name='ResNet-18v1 (ONNX)', archive='resnet18v1.tar.gz', member='resnet18v1/resnet18v1.onnx', sha='9d96d7142c5ce43aa61ce67124b8eb5530afff4c', filename='onnx/models/resnet18v1.onnx'), Model( name='ResNet-18v1 (ONNX)', archive='resnet18v1.tar.gz', member='resnet18v1/test_data_set_0/input_0.pb', sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a', filename='onnx/data/input_resnet18v1.pb'), Model( name='ResNet-18v1 (ONNX)', archive='resnet18v1.tar.gz', member='resnet18v1/test_data_set_0/output_0.pb', sha='70e0ad583cf922452ac6e52d882b5127db086a45', filename='onnx/data/output_resnet18v1.pb'), Model( name='ResNet-50v1 (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v1/resnet50v1.tar.gz', sha='a4ac2da7e0024d61fdb80481496ba966b48b9fea', filename='resnet50v1.tar.gz'), Model( name='ResNet-50v1 (ONNX)', archive='resnet50v1.tar.gz', member='resnet50v1/resnet50v1.onnx', sha='06aa26c6de448e11c64cd80cf06f5ab01de2ec9b', filename='onnx/models/resnet50v1.onnx'), Model( name='ResNet-50v1 (ONNX)', archive='resnet50v1.tar.gz', member='resnet50v1/test_data_set_0/input_0.pb', sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a', filename='onnx/data/input_resnet50v1.pb'), Model( name='ResNet-50v1 (ONNX)', archive='resnet50v1.tar.gz', member='resnet50v1/test_data_set_0/output_0.pb', sha='40deb324ddba7db4117568e1e3911e7a771fb260', filename='onnx/data/output_resnet50v1.pb'), Model( name='ResNet50-Int8 (ONNX)', url='https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v1-12-int8.tar.gz', sha='2ff2a58f4a27362ee6234915452e86287cdcf269', filename='resnet50-v1-12-int8.tar.gz'), Model( name='ResNet50-Int8 (ONNX)', archive='resnet50-v1-12-int8.tar.gz', member='resnet50-v1-12-int8/resnet50-v1-12-int8.onnx', sha='5fbeac70e1a3af3253c21e0e4008a784aa61929f', filename='onnx/models/resnet50_int8.onnx'), Model( name='ResNet50-Int8 (ONNX)', archive='resnet50-v1-12-int8.tar.gz', member='resnet50-v1-12-int8/test_data_set_0/input_0.pb', sha='0946521c8afcfea9340390298a41fb11496b3556', filename='onnx/data/input_resnet50_int8.pb'), Model( name='ResNet50-Int8 (ONNX)', archive='resnet50-v1-12-int8.tar.gz', member='resnet50-v1-12-int8/test_data_set_0/output_0.pb', sha='6d45d2f06150e9045631c7928093728b07c8b12d', filename='onnx/data/output_resnet50_int8.pb'), Model( name='ssd_mobilenet_v1_ppn_coco (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz', sha='549ae0fd82c202786abe53c306b191c578599c44', filename='ssd_mobilenet_v1_ppn_coco.tar.gz'), Model( name='ssd_mobilenet_v1_ppn_coco (TensorFlow)', archive='ssd_mobilenet_v1_ppn_coco.tar.gz', member='ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03/frozen_inference_graph.pb', sha='7943c51c6305b38173797d4afbf70697cf57ab48', filename='ssd_mobilenet_v1_ppn_coco.pb'), Model( name='ResNet101_DUC_HDC (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/duc/ResNet101_DUC_HDC.tar.gz', sha='f8314f381939d01045ac31dbb53d7d35fe3ff9a0', filename='ResNet101_DUC_HDC.tar.gz'), Model( name='ResNet101_DUC_HDC (ONNX)', archive='ResNet101_DUC_HDC.tar.gz', member='ResNet101_DUC_HDC/ResNet101_DUC_HDC.onnx', sha='83f9cefdf3606a37dd4901a925bb9116795dae39', filename='onnx/models/resnet101_duc_hdc.onnx'), Model( name='ResNet101_DUC_HDC (ONNX)', archive='ResNet101_DUC_HDC.tar.gz', member='ResNet101_DUC_HDC/test_data_set_0/input_0.pb', sha='099d0e32742a2fa6a69c329f1bff699fb7266b33', filename='onnx/data/input_resnet101_duc_hdc.pb'), Model( name='ResNet101_DUC_HDC (ONNX)', archive='ResNet101_DUC_HDC.tar.gz', member='ResNet101_DUC_HDC/test_data_set_0/output_0.pb', sha='3713a21bb7228d3179721810bb72565aebee7033', filename='onnx/data/output_resnet101_duc_hdc.pb'), Model( name='TinyYolov2 (ONNX)', url='https://www.cntk.ai/OnnxModels/tiny_yolov2/opset_1/tiny_yolov2.tar.gz', sha='b9102abb8fa6f51368119b52146c30189353164a', filename='tiny_yolov2.tar.gz'), Model( name='TinyYolov2 (ONNX)', archive='tiny_yolov2.tar.gz', member='tiny_yolov2/model.onnx', sha='433fecbd32ac8b9be6f5ee10c39dcecf9dc5c151', filename='onnx/models/tiny_yolo2.onnx'), Model( name='TinyYolov2 (ONNX)', archive='tiny_yolov2.tar.gz', member='tiny_yolov2/test_data_set_0/input_0.pb', sha='a0412fde98ca21d726c0c86ef007c11aa4678e3c', filename='onnx/data/input_tiny_yolo2.pb'), Model( name='TinyYolov2 (ONNX)', archive='tiny_yolov2.tar.gz', member='tiny_yolov2/test_data_set_0/output_0.pb', sha='f9be0446cac76fe38bb23cb09ed23c317907f505', filename='onnx/data/output_tiny_yolo2.pb'), Model( name='CNN Mnist (ONNX)', url='https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz', sha='8bcd3372e44bd95dc8a211bc31fb3025d8edf9f9', filename='mnist.tar.gz'), Model( name='CNN Mnist (ONNX)', archive='mnist.tar.gz', member='mnist/model.onnx', sha='e4fb4914cd1d9e0faed3294e5cecfd1847339763', filename='onnx/models/cnn_mnist.onnx'), Model( name='CNN Mnist (ONNX)', archive='mnist.tar.gz', member='mnist/test_data_set_0/input_0.pb', sha='023f6c94951ab386957964e39727aa43d8c45ea8', filename='onnx/data/input_cnn_mnist.pb'), Model( name='CNN Mnist (ONNX)', archive='mnist.tar.gz', member='mnist/test_data_set_0/output_0.pb', sha='79f3028d97df835b058849d357e06d4c0bfcf5b3', filename='onnx/data/output_cnn_mnist.pb'), Model( name='MobileNetv2 (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/mobilenetv2-1.0.tar.gz', sha='7f1429a8e1f3118a05943ff3ed54dbc9eb55691a', filename='mobilenetv2-1.0.tar.gz'), Model( name='MobileNetv2 (ONNX)', archive='mobilenetv2-1.0.tar.gz', member='mobilenetv2-1.0/mobilenetv2-1.0.onnx', sha='80c97941c3ce34d05bc3d3c9d6e04c44c15906bc', filename='onnx/models/mobilenetv2.onnx'), Model( name='MobileNetv2 (ONNX)', archive='mobilenetv2-1.0.tar.gz', member='mobilenetv2-1.0/test_data_set_0/input_0.pb', sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a', filename='onnx/data/input_mobilenetv2.pb'), Model( name='MobileNetv2 (ONNX)', archive='mobilenetv2-1.0.tar.gz', member='mobilenetv2-1.0/test_data_set_0/output_0.pb', sha='7e58c6faca7fc3b844e18364ae92606aa3f0b18e', filename='onnx/data/output_mobilenetv2.pb'), Model( name='LResNet100E-IR (ONNX)', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100/resnet100.tar.gz', sha='b1178813b705d9d44ed806aa442f0b1cb11aea0a', filename='resnet100.tar.gz'), Model( name='LResNet100E-IR (ONNX)', archive='resnet100.tar.gz', member='resnet100/resnet100.onnx', sha='d307e426cf55cddf9f9292b5ffabb474eec93638', filename='onnx/models/LResNet100E_IR.onnx'), Model( name='LResNet100E-IR (ONNX)', archive='resnet100.tar.gz', member='resnet100/test_data_set_0/input_0.pb', sha='d80a849e000907734bd0061ba570f734784f7d38', filename='onnx/data/input_LResNet100E_IR.pb'), Model( name='LResNet100E-IR (ONNX)', archive='resnet100.tar.gz', member='resnet100/test_data_set_0/output_0.pb', sha='f54c73699d00b18b5c40e4ea895b1e88e7f8dea3', filename='onnx/data/output_LResNet100E_IR.pb'), Model( name='Emotion FERPlus (ONNX)', url='https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz', sha='9ff80899c0cd468999db5d8ffde98780ef85455e', filename='emotion_ferplus.tar.gz'), Model( name='Emotion FERPlus (ONNX)', archive='emotion_ferplus.tar.gz', member='emotion_ferplus/model.onnx', sha='2ef5b3a6404a5feb8cc396d66c86838c4c750a7e', filename='onnx/models/emotion_ferplus.onnx'), Model( name='Emotion FERPlus (ONNX)', archive='emotion_ferplus.tar.gz', member='emotion_ferplus/test_data_set_0/input_0.pb', sha='29621536528116fc12f02bc81c7265f7ffe7c8bb', filename='onnx/data/input_emotion_ferplus.pb'), Model( name='Emotion FERPlus (ONNX)', archive='emotion_ferplus.tar.gz', member='emotion_ferplus/test_data_set_0/output_0.pb', sha='54f7892240d2d9298f5a8064a46fc3a8987015a5', filename='onnx/data/output_emotion_ferplus.pb'), Model( name='Squeezenet (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz', sha='57348321d4d460c07c41af814def3abe728b3a03', filename='squeezenet.tar.gz'), Model( name='Squeezenet (ONNX)', archive='squeezenet.tar.gz', member='squeezenet/model.onnx', sha='c3f272e672fa64a75fb4a2e48dd2ca25fcc76c49', filename='onnx/models/squeezenet.onnx'), Model( name='Squeezenet (ONNX)', archive='squeezenet.tar.gz', member='squeezenet/test_data_set_0/input_0.pb', sha='55c285cfbc4d61e3c026302a3af9e7d220b82d0a', filename='onnx/data/input_squeezenet.pb'), Model( name='Squeezenet (ONNX)', archive='squeezenet.tar.gz', member='squeezenet/test_data_set_0/output_0.pb', sha='e4f3c0c989cc7025ca94759492508d8f4ef3287b', filename='onnx/data/output_squeezenet.pb'), Model( name='DenseNet121 (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/densenet121.tar.gz', sha='338b70e871e73b0550fc8ccc0863b8382e90e8e5', filename='densenet121.tar.gz'), Model( name='DenseNet121 (ONNX)', archive='densenet121.tar.gz', member='densenet121/model.onnx', sha='2874279d0f56f15f4e7e9208526c1b35d85d5ad1', filename='onnx/models/densenet121.onnx'), Model( name='DenseNet121 (ONNX)', archive='densenet121.tar.gz', member='densenet121/test_data_set_0/input_0.pb', sha='d6146a5b08a85309a3b8ada313ae5887c2aa7e3e', filename='onnx/data/input_densenet121.pb'), Model( name='DenseNet121 (ONNX)', archive='densenet121.tar.gz', member='densenet121/test_data_set_0/output_0.pb', sha='f1fd0d5e8d48aff3df2c5c809ea24e982d72028e', filename='onnx/data/output_densenet121.pb'), Model( name='Inception v1 (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/inception_v1.tar.gz', sha='94ecb2bd1426704dca578dc746e3c27bedf22352', filename='inception_v1.tar.gz'), Model( name='Inception v1 (ONNX)', archive='inception_v1.tar.gz', member='inception_v1/model.onnx', sha='f45896d8d35248a62ea551db922d982a90214517', filename='onnx/models/inception_v1.onnx'), Model( name='Inception v1 (ONNX)', archive='inception_v1.tar.gz', member='inception_v1/test_data_set_0/input_0.pb', sha='7ec7a82aa2fecd2c875b7b198ecd9a428bc9f462', filename='onnx/data/input_inception_v1.pb'), Model( name='Inception v1 (ONNX)', archive='inception_v1.tar.gz', member='inception_v1/test_data_set_0/output_0.pb', sha='870a30306bd2b82d5393a0ff5570b022681ef7b6', filename='onnx/data/output_inception_v1.pb'), Model( name='Inception v2 (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_8/inception_v2.tar.gz', sha='d07a442a84d939232c37c976fd8d624fa9f82026', filename='inception_v2.tar.gz'), Model( name='Inception v2 (ONNX)', archive='inception_v2.tar.gz', member='inception_v2/model.onnx', sha='cfa84f36bcae8910e0875872383991cb0c3b9a80', filename='onnx/models/inception_v2.onnx'), Model( name='Inception v2 (ONNX)', archive='inception_v2.tar.gz', member='inception_v2/test_data_set_0/input_0.pb', sha='f4ed6d838c20dbfc3bcf6abfd23c78d74892a5fe', filename='onnx/data/input_inception_v2.pb'), Model( name='Inception v2 (ONNX)', archive='inception_v2.tar.gz', member='inception_v2/test_data_set_0/output_0.pb', sha='cb75fb6db82290c49879380ce72c71e17eda76d0', filename='onnx/data/output_inception_v2.pb'), Model( name='Shufflenet (ONNX)', url='https://s3.amazonaws.com/download.onnx/models/opset_9/shufflenet.tar.gz', sha='c99afcb7fcc809c0688cc99cb3709a052fde1de7', filename='shufflenet.tar.gz'), Model( name='Shufflenet (ONNX)', archive='shufflenet.tar.gz', member='shufflenet/model.onnx', sha='a781faf9f1fe6d001cd7b6b5a7d1a228da0ff17b', filename='onnx/models/shufflenet.onnx'), Model( name='Shufflenet (ONNX)', archive='shufflenet.tar.gz', member='shufflenet/test_data_set_0/input_0.pb', sha='27d31be9a084c1d1d1eacbd766f4c43d59352a07', filename='onnx/data/input_shufflenet.pb'), Model( name='Shufflenet (ONNX)', archive='shufflenet.tar.gz', member='shufflenet/test_data_set_0/output_0.pb', sha='6a33ed6ccef4c69a27a3993363c3f854d0f79bb0', filename='onnx/data/output_shufflenet.pb'), Model( name='ResNet-34_kinetics (ONNX)', # https://github.com/kenshohara/video-classification-3d-cnn-pytorch url='https://www.dropbox.com/s/065l4vr8bptzohb/resnet-34_kinetics.onnx?dl=1', sha='88897629e4abb0fddef939f0c2d668a4edeb0788', filename='resnet-34_kinetics.onnx'), Model( name='Alexnet Facial Keypoints (ONNX)', # https://github.com/ismalakazel/Facial-Keypoint-Detection url='https://drive.google.com/uc?export=dowload&id=1etGXT9WQK1KjDkJ0pUTH-CaHHva4p9cY', sha='e1b82b56b59ab96b50189e1b39487d91d4fa0eea', filename='onnx/models/facial_keypoints.onnx'), Model( name='LightWeight Human Pose Estimation (ONNX)', # https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch url='https://drive.google.com/uc?export=dowload&id=1--Ij_gIzCeNA488u5TA4FqWMMdxBqOji', sha='5960f7aef233d75f8f4020be1fd911b2d93fbffc', filename='onnx/models/lightweight_pose_estimation_201912.onnx'), Model( name='EfficientDet-D0', # https://github.com/google/automl url='https://www.dropbox.com/s/9mqp99fd2tpuqn6/efficientdet-d0.pb?dl=1', sha='f178cc17b44e3ed2f3956a0adc1800a7d2a3b3ae', filename='efficientdet-d0.pb'), Model( name='YOLOv4', # https://github.com/opencv/opencv/issues/17148 downloader=GDrive('1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT'), sha='0143deb6c46fcc7f74dd35bf3c14edc3784e99ee', filename='yolov4.weights'), Model( name='YOLOv4-tiny', # https://github.com/opencv/opencv/issues/17148 url='https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights', sha='d110379b7b86899226b591ad4affc7115f707157', filename='yolov4-tiny.weights'), Model( name='YOLOv4x-mish', # https://github.com/opencv/opencv/issues/18975 url='https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights', sha='a6f2879af2241de2e9730d317a55db6afd0af00b', filename='yolov4x-mish.weights'), Model( name='GSOC2016-GOTURN', # https://github.com/opencv/opencv_contrib/issues/941 downloader=GDrive('1j4UTqVE4EGaUFiK7a5I_CYX7twO9c5br'), sha='49776d262993c387542f84d9cd16566840404f26', filename='gsoc2016-goturn/goturn.caffemodel'), Model( name='DaSiamRPM Tracker network (ONNX)', url='https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=1', sha='91b774fce7df4c0e4918469f0f482d9a27d0e2d4', filename='onnx/models/dasiamrpn_model.onnx'), Model( name='DaSiamRPM Tracker kernel_r1 (ONNX)', url='https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=1', sha='bb64620a54348657133eb28be2d3a2a8c76b84b3', filename='onnx/models/dasiamrpn_kernel_r1.onnx'), Model( name='DaSiamRPM Tracker kernel_cls1 (ONNX)', url='https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=1', sha='e9ccd270ce8059bdf7ed0d1845c03ef4a951ee0f', filename='onnx/models/dasiamrpn_kernel_cls1.onnx'), Model( name='crnn', url='https://drive.google.com/uc?export=dowload&id=1ooaLR-rkTl8jdpGy1DoQs0-X0lQsB6Fj', sha='270d92c9ccb670ada2459a25977e8deeaf8380d3', filename='onnx/models/crnn.onnx'), Model( name='DB_TD500_resnet50', url='https://drive.google.com/uc?export=dowload&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR', sha='1b4dd21a6baa5e3523156776970895bd3db6960a', filename='onnx/models/DB_TD500_resnet50.onnx'), Model( name='YuNet', url='https://github.com/ShiqiYu/libfacedetection.train/raw/7a9738d6ca7bc4a3216578b06a739126435d40ef/tasks/task1/onnx/yunet.onnx', sha='49c52f484b1895e8298dc59e37f262ba7841a601', filename='onnx/models/yunet-202109.onnx'), Model( name='face_recognizer_fast', url='https://drive.google.com/uc?export=dowload&id=1ClK9WiB492c5OZFKveF3XiHCejoOxINW', sha='12ff8b1f5c8bff62e8dd91eabdacdfc998be255e', filename='onnx/models/face_recognizer_fast.onnx'), ] # Note: models will be downloaded to current working directory # expected working directory is /dnn if __name__ == '__main__': selected_model_name = None if len(sys.argv) > 1: selected_model_name = sys.argv[1] print('Model: ' + selected_model_name) failedModels = [] for m in models: print(m) if selected_model_name is not None and not m.name.startswith(selected_model_name): continue if not m.get(): failedModels.append(m.filename) if failedModels: print("Following models have not been downloaded:") for f in failedModels: print("* {}".format(f)) exit(15)