diff --git a/README.md b/README.md
index 00edfca..b47dda0 100644
--- a/README.md
+++ b/README.md
@@ -1,61 +1,45 @@
-
-
-> PIQA is not endorsed by Facebook, Inc.; PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.
+
# PyTorch Image Quality Assessment
-The `piqa` package is a collection of measures and metrics for image quality assessment in various image processing tasks such as denoising, super-resolution, image interpolation, etc. It relies only on [PyTorch](https://github.com/pytorch/pytorch) and takes advantage of its efficiency and automatic differentiation.
-
-PIQA is directly inspired from the [`piq`](https://github.com/photosynthesis-team/piq) project, but focuses on the conciseness, readability and understandability of its (sub-)modules, such that anyone can easily reuse and/or adapt them to its needs.
-
-However, conciseness should never be at the expense of efficiency; PIQA's implementations are up to 3 times faster than those of other IQA PyTorch packages like [`kornia`](https://github.com/kornia/kornia), [`piq`](https://github.com/photosynthesis-team/piq) and [`IQA-pytorch`](https://github.com/dingkeyan93/IQA-optimization).
+PIQA is a collection of PyTorch metrics for image quality assessment in various image processing tasks such as generation, denoising, super-resolution, interpolation, etc. It focuses on the efficiency, conciseness and understandability of its (sub-)modules, such that anyone can easily reuse and/or adapt them to its needs.
> PIQA should be pronounced *pika* (like Pikachu ⚡️)
## Installation
-The `piqa` package is available on [PyPI](https://pypi.org/project/piqa/), which means it is installable with `pip`:
+The `piqa` package is available on [PyPI](https://pypi.org/project/piqa), which means it is installable via `pip`.
-```bash
+```
pip install piqa
```
-Alternatively, if you need the latest features, you can install it using
+Alternatively, if you need the latest features, you can install it from the repository.
-```bash
-pip install git+https://github.com/francois-rozet/piqa
```
-
-or copy the package directly to your project, with
-
-```bash
-git clone https://github.com/francois-rozet/piqa
-cp -R piqa/piqa /piqa
+pip install git+https://github.com/francois-rozet/piqa
```
## Getting started
-In `piqa`, each metric is associated to a class, child of `torch.nn.Module`, which has to be instantiated to evaluate the metric.
+In `piqa`, each metric is associated to a class, child of `torch.nn.Module`, which has to be instantiated to evaluate the metric. All metrics are differentiable and support CPU and GPU (CUDA).
```python
import torch
+import piqa
# PSNR
-from piqa import PSNR
-
x = torch.rand(5, 3, 256, 256)
y = torch.rand(5, 3, 256, 256)
-psnr = PSNR()
+psnr = piqa.PSNR()
l = psnr(x, y)
# SSIM
-from piqa import SSIM
-
x = torch.rand(5, 3, 256, 256, requires_grad=True).cuda()
y = torch.rand(5, 3, 256, 256).cuda()
-ssim = SSIM().cuda()
+ssim = piqa.SSIM().cuda()
l = 1 - ssim(x, y)
l.backward()
```
@@ -63,74 +47,58 @@ l.backward()
Like `torch.nn` built-in components, these classes are based on functional definitions of the metrics, which are less user-friendly, but more versatile.
```python
-import torch
-
from piqa.ssim import ssim
from piqa.utils.functional import gaussian_kernel
-x = torch.rand(5, 3, 256, 256)
-y = torch.rand(5, 3, 256, 256)
+kernel = gaussian_kernel(11, sigma=1.5).expand(3, 11, 11)
-kernel = gaussian_kernel(11, sigma=1.5).repeat(3, 1, 1)
-
-l = ssim(x, y, kernel=kernel, channel_avg=False)
+l = 1 - ssim(x, y, kernel=kernel)
```
-For more information about PIQA's features, check out the documentation at [francois-rozet.github.io/piqa/](https://francois-rozet.github.io/piqa/).
-
-### Metrics
+For more information, check out the documentation at [piqa.readthedocs.io](https://piqa.readthedocs.io).
-| Acronym | Class | Range | Objective | Year | Metric |
-|:-------:|:---------:|:--------:|:---------:|:----:|------------------------------------------------------------------------------------------------------|
-| TV | `TV` | `[0, ∞]` | / | 1937 | [Total Variation](https://en.wikipedia.org/wiki/Total_variation) |
-| PSNR | `PSNR` | `[0, ∞]` | max | / | [Peak Signal-to-Noise Ratio](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio) |
-| SSIM | `SSIM` | `[0, 1]` | max | 2004 | [Structural Similarity](https://en.wikipedia.org/wiki/Structural_similarity) |
-| MS-SSIM | `MS_SSIM` | `[0, 1]` | max | 2004 | [Multi-Scale Structural Similarity](https://ieeexplore.ieee.org/document/1292216/) |
-| LPIPS | `LPIPS` | `[0, ∞]` | min | 2018 | [Learned Perceptual Image Patch Similarity](https://arxiv.org/abs/1801.03924) |
-| GMSD | `GMSD` | `[0, ∞]` | min | 2013 | [Gradient Magnitude Similarity Deviation](https://arxiv.org/abs/1308.3052) |
-| MS-GMSD | `MS_GMSD` | `[0, ∞]` | min | 2017 | [Multi-Scale Gradient Magnitude Similarity Deviation](https://ieeexplore.ieee.org/document/7952357) |
-| MDSI | `MDSI` | `[0, ∞]` | min | 2016 | [Mean Deviation Similarity Index](https://arxiv.org/abs/1608.07433) |
-| HaarPSI | `HaarPSI` | `[0, 1]` | max | 2018 | [Haar Perceptual Similarity Index](https://arxiv.org/abs/1607.06140) |
-| VSI | `VSI` | `[0, 1]` | max | 2014 | [Visual Saliency-based Index](https://ieeexplore.ieee.org/document/6873260) |
-| FSIM | `FSIM` | `[0, 1]` | max | 2011 | [Feature Similarity](https://ieeexplore.ieee.org/document/5705575) |
+### Available metrics
-### JIT
+| Class | Range | Objective | Year | Metric |
+|:---------:|:------:|:---------:|:----:|------------------------------------------------------------------------------------------------------|
+| `TV` | [0, ∞] | / | 1937 | [Total Variation](https://en.wikipedia.org/wiki/Total_variation) |
+| `PSNR` | [0, ∞] | max | / | [Peak Signal-to-Noise Ratio](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio) |
+| `SSIM` | [0, 1] | max | 2004 | [Structural Similarity](https://en.wikipedia.org/wiki/Structural_similarity) |
+| `MS_SSIM` | [0, 1] | max | 2004 | [Multi-Scale Structural Similarity](https://ieeexplore.ieee.org/document/1292216/) |
+| `LPIPS` | [0, ∞] | min | 2018 | [Learned Perceptual Image Patch Similarity](https://arxiv.org/abs/1801.03924) |
+| `GMSD` | [0, ∞] | min | 2013 | [Gradient Magnitude Similarity Deviation](https://arxiv.org/abs/1308.3052) |
+| `MS_GMSD` | [0, ∞] | min | 2017 | [Multi-Scale Gradient Magnitude Similarity Deviation](https://ieeexplore.ieee.org/document/7952357) |
+| `MDSI` | [0, ∞] | min | 2016 | [Mean Deviation Similarity Index](https://arxiv.org/abs/1608.07433) |
+| `HaarPSI` | [0, 1] | max | 2018 | [Haar Perceptual Similarity Index](https://arxiv.org/abs/1607.06140) |
+| `VSI` | [0, 1] | max | 2014 | [Visual Saliency-based Index](https://ieeexplore.ieee.org/document/6873260) |
+| `FSIM` | [0, 1] | max | 2011 | [Feature Similarity](https://ieeexplore.ieee.org/document/5705575) |
+| `FID` | [0, ∞] | min | 2017 | [Fréchet Inception Distance](https://arxiv.org/abs/1706.08500) |
-Most functional components of `piqa` support PyTorch's JIT, *i.e.* [TorchScript](https://pytorch.org/docs/stable/jit.html), which is a way to create serializable and optimizable functions from PyTorch code.
+### Tracing
-By default, jitting is disabled for those components. To enable it, the `PIQA_JIT` environment variable has to be set to `1`. To do so temporarily,
+All metrics of `piqa` support [PyTorch's tracing](https://pytorch.org/docs/stable/generated/torch.jit.trace.html), which optimizes their execution, especially on GPU.
-* UNIX-like `bash`
-
-```bash
-export PIQA_JIT=1
-```
-
-* Windows `cmd`
-
-```cmd
-set PIQA_JIT=1
-```
-
-* Microsoft `PowerShell`
+```python
+ssim = piqa.SSIM().cuda()
+ssim_traced = torch.jit.trace(ssim, (x, y))
-```powershell
-$env:PIQA_JIT=1
+l = 1 - ssim_traced(x, y) # should be faster ¯\_(ツ)_/¯
```
### Assert
-PIQA uses type assertions to raise meaningful messages when an object-oriented component doesn't receive an input of the expected type. This feature eases a lot early prototyping and debugging, but it might hurt a little the performances.
-
-If you need the absolute best performances, the assertions can be disabled with the Python flag [`-O`](https://docs.python.org/3/using/cmdline.html#cmdoption-o). For example,
+PIQA uses type assertions to raise meaningful messages when a metric doesn't receive an input of the expected type. This feature eases a lot early prototyping and debugging, but it might hurt a little the performances. If you need the absolute best performances, the assertions can be disabled with the Python flag [`-O`](https://docs.python.org/3/using/cmdline.html#cmdoption-o). For example,
-```bash
+```
python -O your_awesome_code_using_piqa.py
```
Alternatively, you can disable PIQA's type assertions within your code with
```python
-from piqa.utils import set_debug
-set_debug(False)
+piqa.utils.set_debug(False)
```
+
+## Contributing
+
+If you have a question, an issue or would like to contribute, please read our [contributing guidelines](https://github.com/francois-rozet/piqa/blob/master/CONTRIBUTING.md).