mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 06:10:04 +06:00
76c4d8bf26
* ✨ add pytorch image classification example * 🔥 remove utils.py * 💄 fix flake8 style issues * 🔥 remove unnecessary line * ✨ limit dataset sizes * 📌 update reqs * 🎨 restructure - use datasets lib * 🎨 import transforms directly * 📝 add comments * 💄 style * 🔥 remove flag * 📌 update requirement warning * 📝 add vision README.md * 📝 update README.md * 📝 update README.md * 🎨 add image-classification tag to model card * 🚚 rename vision ➡️ image-classification * 📝 update image-classification README.md
133 lines
3.7 KiB
Markdown
133 lines
3.7 KiB
Markdown
<!---
|
|
Copyright 2021 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
-->
|
|
|
|
# Image classification examples
|
|
|
|
The following examples showcase how to fine-tune a `ViT` for image-classification using PyTorch.
|
|
|
|
## Using datasets from 🤗 `datasets`
|
|
|
|
Here we show how to fine-tune a `ViT` on the [beans](https://huggingface.co/datasets/beans) dataset.
|
|
|
|
👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).
|
|
|
|
```bash
|
|
python run_image_classification.py \
|
|
--dataset_name beans \
|
|
--output_dir ./beans_outputs/ \
|
|
--remove_unused_columns False \
|
|
--do_train \
|
|
--do_eval \
|
|
--push_to_hub \
|
|
--push_to_hub_model_id vit-base-beans \
|
|
--learning_rate 2e-5 \
|
|
--num_train_epochs 5 \
|
|
--per_device_train_batch_size 8 \
|
|
--per_device_eval_batch_size 8 \
|
|
--logging_strategy steps \
|
|
--logging_steps 10 \
|
|
--evaluation_strategy epoch \
|
|
--save_strategy epoch \
|
|
--load_best_model_at_end True \
|
|
--save_total_limit 3 \
|
|
--seed 1337
|
|
```
|
|
|
|
Here we show how to fine-tune a `ViT` on the [cats_vs_dogs](https://huggingface.co/datasets/cats_vs_dogs) dataset.
|
|
|
|
👀 See the results here: [nateraw/vit-base-cats-vs-dogs](https://huggingface.co/nateraw/vit-base-cats-vs-dogs).
|
|
|
|
```bash
|
|
python run_image_classification.py \
|
|
--dataset_name cats_vs_dogs \
|
|
--output_dir ./cats_vs_dogs_outputs/ \
|
|
--remove_unused_columns False \
|
|
--do_train \
|
|
--do_eval \
|
|
--push_to_hub \
|
|
--push_to_hub_model_id vit-base-cats-vs-dogs \
|
|
--fp16 True \
|
|
--learning_rate 2e-4 \
|
|
--num_train_epochs 5 \
|
|
--per_device_train_batch_size 32 \
|
|
--per_device_eval_batch_size 32 \
|
|
--logging_strategy steps \
|
|
--logging_steps 10 \
|
|
--evaluation_strategy epoch \
|
|
--save_strategy epoch \
|
|
--load_best_model_at_end True \
|
|
--save_total_limit 3 \
|
|
--seed 1337
|
|
```
|
|
|
|
## Using your own data
|
|
|
|
To use your own dataset, the training script expects the following directory structure:
|
|
|
|
```bash
|
|
root/dog/xxx.png
|
|
root/dog/xxy.png
|
|
root/dog/[...]/xxz.png
|
|
|
|
root/cat/123.png
|
|
root/cat/nsdf3.png
|
|
root/cat/[...]/asd932_.png
|
|
```
|
|
|
|
Once you've prepared your dataset, you can can run the script like this:
|
|
|
|
```bash
|
|
python run_image_classification.py \
|
|
--dataset_name nateraw/image-folder \
|
|
--train_dir <path-to-train-root> \
|
|
--output_dir ./outputs/ \
|
|
--remove_unused_columns False \
|
|
--do_train \
|
|
--do_eval
|
|
```
|
|
|
|
### 💡 The above will split the train dir into training and evaluation sets
|
|
- To control the split amount, use the `--train_val_split` flag.
|
|
- To provide your own validation split in its own directory, you can pass the `--validation_dir <path-to-val-root>` flag.
|
|
|
|
|
|
## Sharing your model on 🤗 Hub
|
|
|
|
0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account
|
|
|
|
1. Make sure you have `git-lfs` installed and git set up.
|
|
|
|
```bash
|
|
$ apt install git-lfs
|
|
$ git config --global user.email "you@example.com"
|
|
$ git config --global user.name "Your Name"
|
|
```
|
|
|
|
2. Log in with your HuggingFace account credentials using `huggingface-cli`
|
|
|
|
```bash
|
|
$ huggingface-cli login
|
|
# ...follow the prompts
|
|
```
|
|
|
|
3. When running the script, pass the following arguments:
|
|
|
|
```bash
|
|
python run_image_classification.py \
|
|
--push_to_hub \
|
|
--push_to_hub_model_id <name-your-model> \
|
|
...
|
|
``` |