
## Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long
Keywords: 
JMLR/2022/Proceedings/211251 - Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs.pdf
Project URL: https://github.com/thuml/LogME

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[2]

The authors state a link to their implementation on the JMLR website and in section 6 (https://github.com/thuml/LogME). In the readme the authors state many examples how to use the code, where to download datasets and contact details. No installation instructions. Code has good comments.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[2]

(10/10)

The authors conduct an experiment on synthetic toy data in 6.1 and describe the process, but only use it to illustrate and not for the conclusion (skipped). In 6.2. they rank pretrained models, cite each they rank and they download in the implementation from PyTorch directly. The authors use 9 benchmarkdatasets and cite each one and provide details on each in appendix F but more details would be welcome. In 6/4 they also use ImageNet-1k.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

The authors use grid-search for LR and weight decay and provide the grids. Not all HPs are tuned, although this is motivated with a citation.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors use the provided t/v/t splits of the datasets or 60/20/20 otherwise. The authors eavaluate LogME, MSE, F1 score and accuracy. Results are single model.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[4]

Requries expertise on model ranking/tuning and transfer learning.
