Add image classification script, no trainer (#16727)
* Add first draft * Improve README and run fixup * Make script aligned with other scripts, improve README * Improve script and add test * Remove print statement * Apply suggestions from code review * Add num_labels to make test pass * Improve README
This commit is contained in:
NielsRogge
@@ -14,21 +14,28 @@ See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
# Image classification example
|
||||
# Image classification examples
|
||||
|
||||
This directory contains a script, `run_image_classification.py`, that showcases how to fine-tune any model supported by the [`AutoModelForImageClassification` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) (such as [ViT](https://huggingface.co/docs/transformers/main/en/model_doc/vit), [ConvNeXT](https://huggingface.co/docs/transformers/main/en/model_doc/convnext), [ResNet](https://huggingface.co/docs/transformers/main/en/model_doc/resnet), [Swin Transformer](https://huggingface.co/docs/transformers/main/en/model_doc/swin)...) using PyTorch. It can be used to fine-tune models on both well-known datasets (like [CIFAR-10](https://huggingface.co/datasets/cifar10), [Fashion MNIST](https://huggingface.co/datasets/fashion_mnist), ...) as well as on your own custom data.
|
||||
This directory contains 2 scripts that showcase how to fine-tune any model supported by the [`AutoModelForImageClassification` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification) (such as [ViT](https://huggingface.co/docs/transformers/main/en/model_doc/vit), [ConvNeXT](https://huggingface.co/docs/transformers/main/en/model_doc/convnext), [ResNet](https://huggingface.co/docs/transformers/main/en/model_doc/resnet), [Swin Transformer](https://huggingface.co/docs/transformers/main/en/model_doc/swin)...) using PyTorch. They can be used to fine-tune models on both [datasets from the hub](#using-datasets-from-hub) as well as on [your own custom data](#using-your-own-data).
|
||||
|
||||
This page includes 2 sections:
|
||||
- [Using datasets from the 🤗 hub](#using-datasets-from-hub)
|
||||
- [Using your own data](#using-your-own-data).
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_classification_inference_widget.png" height="400" />
|
||||
|
||||
Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224
|
||||
|
||||
## Using datasets from Hub
|
||||
Content:
|
||||
- [PyTorch version, Trainer](#pytorch-version-no-trainer)
|
||||
- [PyTorch version, no Trainer](#pytorch-version-trainer)
|
||||
|
||||
## PyTorch version, Trainer
|
||||
|
||||
Based on the script [`run_image_classification.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py).
|
||||
|
||||
The script leverages the 🤗 [Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer) to automatically take care of the training for you, running on distributed environments right away.
|
||||
|
||||
### Using datasets from Hub
|
||||
|
||||
Here we show how to fine-tune a Vision Transformer (`ViT`) on the [beans](https://huggingface.co/datasets/beans) dataset, to classify the disease type of bean leaves.
|
||||
|
||||
👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).
|
||||
|
||||
```bash
|
||||
python run_image_classification.py \
|
||||
--dataset_name beans \
|
||||
@@ -51,9 +58,11 @@ python run_image_classification.py \
|
||||
--seed 1337
|
||||
```
|
||||
|
||||
To fine-tune another model, simply provide the `--model_name_or_path` argument. To train on another dataset, simply set the `--dataset_name` argument.
|
||||
👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).
|
||||
|
||||
## Using your own data
|
||||
Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with any model or dataset from the [hub](https://huggingface.co/). For an overview of all possible arguments, we refer to the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) of the `TrainingArguments`, which can be passed as flags.
|
||||
|
||||
### Using your own data
|
||||
|
||||
To use your own dataset, there are 2 ways:
|
||||
- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
|
||||
@@ -61,7 +70,7 @@ To use your own dataset, there are 2 ways:
|
||||
|
||||
Below, we explain both in more detail.
|
||||
|
||||
### Provide them as folders
|
||||
#### Provide them as folders
|
||||
|
||||
If you provide your own folders with images, the script expects the following directory structure:
|
||||
|
||||
@@ -88,11 +97,11 @@ python run_image_classification.py \
|
||||
|
||||
Internally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.
|
||||
|
||||
#### 💡 The above will split the train dir into training and evaluation sets
|
||||
##### 💡 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.
|
||||
|
||||
### Upload your data to the hub, as a (possibly private) repo
|
||||
#### Upload your data to the hub, as a (possibly private) repo
|
||||
|
||||
It's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:
|
||||
|
||||
@@ -117,17 +126,18 @@ dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "pa
|
||||
Next, push it to the hub!
|
||||
|
||||
```python
|
||||
# assuming you have ran the huggingface-cli login command in a terminal
|
||||
dataset.push_to_hub("name_of_your_dataset")
|
||||
|
||||
# if you want to push to a private repo, simply pass private=True:
|
||||
dataset.push_to_hub("name_of_your_dataset", private=True)
|
||||
```
|
||||
|
||||
and that's it! You can now simply train your model simply by setting the `--dataset_name` argument to the name of your dataset on the hub (as explained in [Using datasets from the 🤗 hub](#using-datasets-from-hub)).
|
||||
and that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub (as explained in [Using datasets from the 🤗 hub](#using-datasets-from-hub)).
|
||||
|
||||
More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).
|
||||
|
||||
# Sharing your model on 🤗 Hub
|
||||
### Sharing your model on 🤗 Hub
|
||||
|
||||
0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account
|
||||
|
||||
@@ -154,3 +164,46 @@ python run_image_classification.py \
|
||||
--push_to_hub_model_id <name-your-model> \
|
||||
...
|
||||
```
|
||||
|
||||
## PyTorch version, no Trainer
|
||||
|
||||
Based on the script [`run_image_classification_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py).
|
||||
|
||||
Like `run_image_classification.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on an image classification task. The main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
|
||||
|
||||
It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
|
||||
or the dataloaders directly in the script) but still run in a distributed setup, and supports mixed precision by
|
||||
the means of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
|
||||
after installing it:
|
||||
|
||||
```bash
|
||||
pip install accelerate
|
||||
```
|
||||
|
||||
You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
|
||||
|
||||
```bash
|
||||
accelerate config
|
||||
```
|
||||
|
||||
and reply to the questions asked. Then
|
||||
|
||||
```bash
|
||||
accelerate test
|
||||
```
|
||||
|
||||
that will check everything is ready for training. Finally, you can launch training with
|
||||
|
||||
```bash
|
||||
accelerate launch run_image_classification_trainer.py
|
||||
```
|
||||
|
||||
This command is the same and will work for:
|
||||
|
||||
- single/multiple CPUs
|
||||
- single/multiple GPUs
|
||||
- TPUs
|
||||
|
||||
Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
|
||||
|
||||
Regarding using custom data with this script, we refer to [using your own data](#using-your-own-data).
|
||||
Reference in New Issue
Block a user