I was trying to train my detector on the PASCAL VOC dataset. I noticed that the image normalization parameter - std values in ssd512_voc.py and ssd300_voc.py img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) are different from those in faster_rcn...
(480, 640, 3), 'scale_factor': [1., 1., 1., 1.], 'flip': True, 'flip_direction': 'horizontal', 'img_norm_cfg': { 'mean': [123.675, 116.28 , 103.53 ], 'std': [58.395, 57.12 , 57.375], 'to_rgb': True }, 'batch_input_shape': (480, 640) } img_metas = [_img_...
32, 3, len(labelNames)) # check to see if we are using a Keras Functional model elif args["model"] == "functional": # instantiate a Keras Functional model print
driven = drive_source_demo(S, T, cfg_path, pirender_ckpt_path, deep3d_ckpt_path) driven = np.array(driven) elif pose_drive == 'faceVid2Vid': from swap_face_fine.face_vid2vid.drive_demo import init_facevid2vid_pretrained_model, drive_source_demo ...
3 .map('path', 'img', ops.image_decode.cv2('rgb')) 4 .map('img', 'vec', ops.image_text_embedding.clip(model_name='clip_vit_base_patch16', modality='image')) 5 .map('vec', 'vec', lambda x: x/np.linalg.norm(x))
gpu= (0, 1, etc.) # Which GPU to use? default: 0 Resnet-18 Defaults: (layer = 'avgpool', layer_output_size = 512) Layer parameter must be an string representing the name of a layer below conv1=nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False)bn1=nn.BatchNorm2d(...
.installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / ...