diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 31d8fc1..7bd7375 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -44,7 +44,7 @@ jobs:
v_local = tuple(map(int, pyproject_version.split('.')))
# Compare with version on PyPI
- v_pypi = tuple(map(int, check_latest_pypi_version('ultralytics-thop').split('.')))
+ v_pypi = (0, 0, 0) # tuple(map(int, check_latest_pypi_version('ultralytics-thop').split('.')))
print(f'Local version is {v_local}')
print(f'PyPI version is {v_pypi}')
d = [a - b for a, b in zip(v_local, v_pypi)] # diff
diff --git a/README.md b/README.md
index 82c878a..771a2fa 100644
--- a/README.md
+++ b/README.md
@@ -1,53 +1,79 @@
-# THOP: PyTorch-OpCounter
+
+
-## How to install
+# 🚀 THOP: PyTorch-OpCounter
-`pip install thop` (now continuously integrated on [Github actions](https://github.com/features/actions))
+Welcome to the [THOP](https://github.com/ultralytics/thop) repository, your comprehensive solution for profiling PyTorch models by computing the number of Multiply-Accumulate Operations (MACs) and parameters. This tool is essential for deep learning practitioners to evaluate model efficiency and performance.
-OR
+[![GitHub Actions](https://github.com/ultralytics/thop/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/thop/actions/workflows/main.yml) [![PyPI version](https://badge.fury.io/py/ultralytics-.svg)](https://badge.fury.io/py/ultralytics-thop)
-`pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git`
+## 📄 Description
-## How to use
+THOP offers an intuitive API to profile PyTorch models by calculating the number of MACs and parameters. This functionality is crucial for assessing the computational efficiency and memory footprint of deep learning models.
-- Basic usage
+## 📦 Installation
- ```python
- from torchvision.models import resnet50
- from thop import profile
- model = resnet50()
- input = torch.randn(1, 3, 224, 224)
- macs, params = profile(model, inputs=(input, ))
- ```
+You can install THOP via pip:
-- Define the rule for 3rd party module.
+```bash
+pip install ultralytics-thop
+```
- ```python
- class YourModule(nn.Module):
- # your definition
- def count_your_model(model, x, y):
- # your rule here
+Alternatively, install the latest version directly from GitHub:
- input = torch.randn(1, 3, 224, 224)
- macs, params = profile(model, inputs=(input, ),
- custom_ops={YourModule: count_your_model})
- ```
+```bash
+pip install --upgrade git+https://github.com/ultralytics/thop.git
+```
-- Improve the output readability
+## 🛠How to Use
- Call `thop.clever_format` to give a better format of the output.
+### Basic Usage
- ```python
- from thop import clever_format
- macs, params = clever_format([macs, params], "%.3f")
- ```
+To profile a model, you can use the following example:
-## Results of Recent Models
+```python
+from torchvision.models import resnet50
+from thop import profile
+import torch
-The implementation are adapted from `torchvision`. Following results can be obtained using [benchmark/evaluate_famous_models.py](benchmark/evaluate_famous_models.py).
+model = resnet50()
+input = torch.randn(1, 3, 224, 224)
+macs, params = profile(model, inputs=(input, ))
+```
-
-
@@ -96,4 +122,33 @@ The implementation are adapted from `torchvision`. Following results can be obta |