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Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding of digital images and videos.
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被全球140所大学采用教学。
Image-to-Image Translation in PyTorch
Face recognition using Tensorflow
A MNIST-like fashion product database. Benchmark
Interactive deep learning book with code, math, and discussions. Available in multi-frameworks. Adopted at 140 universities from 35 countries.
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.
Hide screen when boss is approaching.
Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
3D Reconstruction Software
Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）
Synthesizing and manipulating 2048x1024 images with conditional GANs
The shape parameter in disk documentation (and maybe others using the same machinery) seems misleading to me. Based on the following phrasing, I would expect shape=None and the shape=dimensions_of_my_disk to lead to equal results. (but it doesn't)
Image shape which is used to determine the maximum extent
A PyTorch Implementation of Single Shot MultiBox Detector
Interactive Image Generation via Generative Adversarial Networks
Well #77 didn't work for me while resuming from checkpoint_18.pth. The problem is when we resume, the model and optimizer passed in the restore_from function are suitable for epoch less than 10 (till backbone is not training) because the cfg.TRAIN.START_EPOCH is 0 (passed in build_opt_lr function just before restore_from) initially so this mismatches the optimizer after backbone start training. So
The most of modules are lacking very useful __repr__ function. I am leaving this issue open until we catch-up.
__repr__ s__repr__ existence
(siammask) [liqiang@inspur siammask]$ bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2016 0
[2019-03-14 19:42:16,619-rk0-test.py#551] Namespace(arch='Custom', config='config_vot.json', dataset='VOT2016', gt=False, log='log_test.txt', mask=True, refine=True, resume='SiamMask_VOT.pth', save_mask=False, visualization=False)
[2019-03-14 19:42:17,087-rk0-load_helper.py# 31] load pretrai
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
How do i resume training for text classification?
[CVPR2020] Adversarial Latent Autoencoders
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
3D ResNets for Action Recognition (CVPR 2018)
Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
The Open Source Framework for Machine Vision
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
Hi, thanks for the great code!
I wonder do you have plans to support resuming from checkpoints for classification? As we all know, in terms of training ImageNet, the training process is really long and it can be interrupted somehow, but I haven't notice any code related to "resume" in
scripts/classification/train_imagenet.py.Maybe @hetong007 ? Thanks in advance.