| Name | fimm JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Finetune PyTorch Image Models with TIMM |
| upload_time | 2024-12-16 11:15:56 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License Copyright (c) 2024 rapanti Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
machine learning
deep learning
image
fine-tuning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# FIMM
Finetune PyTorch Image Models with TIMM
This project provides a simple way to finetune PyTorch Image Models with TIMM.
## Installation
To install FIMM (`fimm`), you can simply use `pip`:
```bash
pip install fimm
```
### Install from source
To install from source, you can clone this repo and install with `pip`:
```bash
git clone https://github.com/rapanti/fimm
pip install -e fimm # -e for editable mode
```
## Usage
To use FIMM, you can simply run the follwing command to train or finetune a model:
```bash
train --data-dir /path/to/dataset --model resnet50 --experiment resnet50 # this trains a resnet50 model from scratch
train --data-dir /path/to/dataset --model resnet50 --experiment resnet50 --pretrained # this finetunes a resnet50 model
```
To validate the performance of a model, you can simply run the following command:
```bash
validate --data-dir /path/to/eval/dataset --model resnet50 --checkpoint output/train/resnet50/model_best.pth.tar # this tests the resnet50 model
```
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