semvec/pypi-packages huggingface dataset:
talos-linux-api-v1-5-0 0.1.0
Python bindings for the Talos Linux gRPC API (v1.5.0)
License: MIT
Requires Python: >=3.7

# `talos-linux-api` - Python bindings for the Talos Linux gRPC API

## Installation
Multiple API versions can be installed simultaneously and are made available
at runtime under an *Implicit Namespace Package* named `talos_linux_api`.

Currently available API versions:

* `pip install talos-linux-api-v1.2.0`
* `pip install talos-linux-api-v1.3.0`
* `pip install talos-linux-api-v1.4.0`
* `pip install talos-linux-api-v1.5.0`


## Usage example
```python
import ssl
from talos_linux_api.v1_5_0.machine import MachineServiceStub
from grpclib.client import Channel
from betterproto.lib.google.protobuf import Empty

ssl_context = ssl.create_default_context()
ssl_context.load_cert_chain('client.crt', 'client.key')
ssl_context.load_verify_locations('ca.crt')

async with Channel(host="example.com", port=50000, ssl=ssl_context) as channel:
    machine_service = MachineServiceStub(channel)
    response = await machine_service.cpu_info(Empty())
```
DeepDC-PyTorch 0.2
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
License: MIT
Keywords: pytorch,similarity,VGG,texture,structure,metric

# DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator

This is the repository of paper [DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator](https://arxiv.org/abs/***). 

### Highlights:

* A novel FR-IQA model that fully utilizes the *texture-sensitiv*e of pre-trained DNN features, which computes **distance correlation** in the deep feature domain 
* The model is **exclusively** based on the features of the pre-trained DNNs and does not rely on fine-tuning with MOSs
* Extensive experiments achieve superior performance on five standard IQA datasets, one perceptual similarity dataset, two texture similarity datasets, and one geometric transformation dataset
* It can be employed as an objective function in texture synthesis and neural style transfer
   



### ====== Pytorch Implementation ======
**Installation:** 
- ```pip install DeepDC-PyTorch```

### Requirements: 
- Python >= 3.6
- PyTorch >= 1.0

**Usage:** 
```python
from DeepDC_PyTorch import DeepDC
model = DeepDC()
# calculate DeepDC between X, Y (a batch of RGB images, data range: 0~1) 
deepdc_score = model(X, Y)
```
or

```bash
git clone https://github.com/h4nwei/DeepDC
cd DeepDC_PyTorch
python DeepDC.py --ref <ref_path> --dist <dist_path>
```


## Reference

- R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in *IEEE Conference on Computer Vision and Pattern Recognition*, 2018, pp. 586–595.
- K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 44, no. 5, pp. 2567–2581, 2020.
- I. Kligvasser, T. Shaham, Y. Bahat, and T. Michaeli, “Deep selfdissimilarities as powerful visual fingerprints,” in *Neural Information Processing Systems*, 2021, pp. 3939–3951.

## Citation
```bibtex
@article{zhu2023DeepDC,
title={DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator},
author={Zhu, Hanwei and Chen, Baoliang and Zhu, Lingyu and Wang, Shiqi and Lin, Weisi},
journal={CoRR},
volume = {abs/2211.04927v2},
year={2023},
url = {https://arxiv.org/pdf/2211.04927v2.pdf}
}