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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
| Date | Stars |
|---|---|
| 2026-07-30 | 3065 |
| 2026-08-10 | 3066 |
| 2026-08-18 | 3064 |
| 2026-08-21 | 3063 |
| 2026-09-01 | 3062 |
| 2026-09-03 | 3062 |
| 2026-09-11 | 3063 |
| 2026-09-19 | 3062 |
| 2026-09-21 | 3061 |
| 2026-09-26 | 3061 |
| 2026-10-09 | 3060 |
| 2026-10-10 | 3061 |
| 2026-10-13 | 3063 |
| 2026-10-14 | 3064 |
| 2026-10-21 | 3063 |
| 2026-10-31 | 3064 |
| 2026-11-04 | 3064 |
Today
— stars today
This week
+1 stars this week
This month
+3 stars this month
Momentum
0.0
growth rate 0.03%/day
# Tencent ML-Images
This repository introduces the open-source project dubbed **Tencent ML-Images**, which publishes
<!--- * **ML-Images**: the largest open-source multi-label image database, including 18,019,881 URLs to images, which are annotated with labels up to 11,166 categories-->
* **ML-Images**: the largest open-source multi-label image database, including 17,609,752 training and 88,739 validation image URLs, which are annotated with up to 11,166 categories
* **Resnet-101 model**: it is pre-trained on ML-Images, and achieves the top-1 accuracy 80.73% on ImageNet via transfer learning
## Updates
* [2019/12/26] Our manuscript of this open-source project has been accepted to IEEE Access ([Journal](https://ieeexplore.ieee.org/document/8918053/authors#authors), [ArXiv](https://arxiv.org/abs/1901.01703)). It presents more details of the database, the loss function, the training algorithm, and more experimental results.
* [2018/12/19] We simplify the procedure of downloading images. Please see [Download Images](#download-images).
<!--- * [2018/12/22] We release one ArXiv manuscript at XXX, to demonstrate the details of our database, the loss function and training algorithm, as well as results. --->
# Contents
* [Dependencies](#dependencies)
* [Data](#data)
* [Image Source](#image-source)
* [Download Images](#download-images)
* [Download Images from ImageNet](#download-images-from-imagenet)
* [Download Images from Open Images](#download-images-from-open-images)
* [Semantic Hierarchy](#semantic-hierarchy)
* [Annotations](#annotations)
* [Statistics](#statistics)
* [Train](#train)
* [Prepare the TFRecord File](#prepare-the-tfrecord-file)
* [Pretrain on ML-Images](#pretrain-on-ml-images)
* [Finetune on ImageNet](#finetune-on-imagenet)
* [Checkpoints](#checkpoints)
* [Single-Label Image Classification](#single-label-image-classification)
* [Feature Extraction](#feature-extraction)
* [Results](#results)
* [Copyright](#copyright)
* [Citation](#citation)
# [Dependencies](#dependencies)
* Linux
* [Python 2.7](https://www.python.org/)
* [Tensorflow >= 1.6.0](https://www.tensorflow.org/install/)
# [Data](#data)
[[back to top](#)]
### [Image Source](#image-source)
[[back to top](#)]
The image URLs of ML-Images are collected from [ImageNet](http://www.image-net.org/) and [Open Images](https://github.com/openimages/dataset).
Specifically,
* Part 1: From the whole database of ImageNet, we adopt 10,706,941 training and 50,000 validation image URLs, covering 10,032 categories.
* Part 2: From Open Images, we adopt 6,902,811 training and 38,739 validation image URLs, covering 1,134 unique categories (note that some other categories are merged with their synonymous categories from ImageNet).
Finally, ML-Images includes 17,609,752 training and 88,739 validation image URLs, covering 11,166 categories.
<!---
The image URLs of ML-Images are collected from [ImageNet](http://www.image-net.org/) and [Open Images](https://github.com/openimages/dataset).
Specifically,
* Part 1: we adopt the set [ImageNet-11k](http://data.mxnet.io/models/imagenet-11k/). It is a subset of ImageNet, collected by [MXNet](http://mxnet.incubator.apache.org/). It includes 1,474,703 images and 11,221 categories. However, we find there are many abstract categories in visual domain, such as "event", "summer", etc. We think that the training images annotated with such abstract categories
will not help (even harm) the visual representation learning. Thus, we abandon these categories.
* Part 2: We filter the URLs of Open Images via a per-class criteria. Firstly, if one class occurs in less than 650 URLs, then it is removed. Besides, we also 1) remove some abstract categories as did above, and 2) merge some redundant categories with those in ImageNet. Then, if all annotated tags of one URL are removed, then this URL is abandoned. Consequently, 6,902,811 training URLs and 159,424 validation URLs are remained, coverExcerpt of 21,081 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:675a171f3d5a8d7d, topic:computer-vision, readme:image classification
matched fp:675a171f3d5a8d7d, topic:deep-learning