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# Vision-Text dual encoder model training examples

> Note: This example is experimental and might not give the best possible results

The following example showcases how to train a CLIP like vision-text dual encoder model
using a pre-trained vision and text encoder using the JAX/Flax backend.

Such a model can be used for natural language image search and potentially zero-shot image classification.
The model is inspired by the [CLIP](https://openai.com/blog/clip/) approach, introduced by Alec Radford et al.
The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their
captions into the same embedding space, such that the caption embeddings are located near the embeddings
of the images they describe.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.

In this example we will use the vision model from [CLIP](https://huggingface.co/models?filter=clip)
as the image encoder and [`roberta-base`](https://huggingface.co/roberta-base) as the text encoder.
Note that one can also use the [ViT](https://huggingface.co/models?filter=vit) model as image encoder and any other BERT or ROBERTa model as text encoder.
To train the model on languages other than English one should choose a text encoder trained on the desired
language and a image-text dataset in that language. One such dataset is [WIT](https://github.com/google-research-datasets/wit).	

Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"clip-roberta-base"`, but you can change the model name as you like.

You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
you are logged in) or via the command line:

```
huggingface-cli repo create clip-roberta-base
```
Next we clone the model repository to add the tokenizer and model files.
```
git clone https://huggingface.co/<your-username>/clip-roberta-base
```
To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.

```
cd clip-roberta-base
git lfs track "*tfevents*"
```

Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the `run_hybrid_clip.py`.

```bash
export MODEL_DIR="./clip-roberta-base
ln -s ~/transformers/examples/flax/summarization/run_hybrid_clip.py run_hybrid_clip.py
```

## How to use the `FlaxHybridCLIP` model:

The `FlaxHybridCLIP` class let's you load any text and vision encoder model to create a dual encoder. 
Here is an example of how to load the model using pre-trained text and vision models.

```python
from modeling_hybrid_clip import FlaxHybridCLIP

model = FlaxHybridCLIP.from_text_vision_pretrained("bert-base-uncased", "openai/clip-vit-base-patch32")

# save the model
model.save_pretrained("bert-clip")

# load the saved model
model = FlaxHybridCLIP.from_pretrained("bert-clip")
```

If the checkpoints are in PyTorch then one could pass `text_from_pt=True` and `vision_from_pt=True`. This will load the model
PyTorch checkpoints convert them to flax and load the model.

```python
model = FlaxHybridCLIP.from_text_vision_pretrained("bert-base-uncased", "openai/clip-vit-base-patch32", text_from_pt=True, vision_from_pt=True)
```

This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
loaded using the pre-trained weights.

## Prepare the dataset

We will use the MS-COCO dataset to train our dual encoder model. MS-COCO contains over 82,000 images, each of which has at least 5 different caption annotations. The dataset is usually used for image captioning tasks, but we can repurpose the image-caption pairs to train our dual encoder model for image search.

### Download and extract the data.

It consists of two compressed folders: one with images, and the other—with associated image captions. Note that the compressed images folder is 13GB in size.

```bash
wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
wget http://images.cocodataset.org/zips/train2014.zip

unzip annotations_trainval2014.zip
unzip train2014.zip

mkdir coco_dataset
mv train2014 coco_dataset/
mv annotations coco_dataset/
```

### Prepare dataset files and split the dataset.

```python
import json
import collections

images_dir = "coco_dataset/train2014"
annotation_file = "coco_dataset/annotations/captions_train2014.json"
with open(annotation_file, "r") as f:
    annotations = json.load(f)["annotations"]

image_path_to_caption = collections.defaultdict(list)
for element in annotations:
    caption = f"{element['caption'].lower().rstrip('.')}"
    image_path = images_dir + "/COCO_train2014_" + "%012d.jpg" % (element["image_id"])
    image_path_to_caption[image_path].append(caption)

lines = []
for image_path, captions in image_path_to_caption.items():
    lines.append(json.dumps({"image_path": image_path, "captions": captions}))

train_lines = lines[:-8000]
valid_line = lines[-8000:]
with open("coco_dataset/train_dataset.json", "w") as f:
    f.write("\n".join(train_lines))

with open("coco_dataset/valid_dataset.json", "w") as f:
    f.write("\n".join(valid_line))
```

> Note: The data loading and processing part of this script can still be improved for maximum performance. In particular one should decode the images beforehand and use those instead decoding them each time. If the dataset is small or if you have huge disk space the you could also pre-process all the dataset beforehand and then use it.

## Train the model
Next we can run the example script to train the model:

```bash
python run_hybrid_clip.py \
    --output_dir ${MODEL_DIR} \
    --text_model_name_or_path="roberta-base" \
    --vision_model_name_or_path="openai/clip-vit-base-patch32" \
    --tokenizer_name="roberta-base" \
    --train_file="coco_dataset/train_dataset.json" \
    --validation_file="coco_dataset/validation_dataset.json" \
    --do_train --do_eval \
    --num_train_epochs="40" --max_seq_length 96 \
    --per_device_train_batch_size="64" \
    --per_device_eval_batch_size="64" \
    --learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \
    --overwrite_output_dir \
    --preprocessing_num_workers 32 \
    --push_to_hub
```

This should finish in ~1h50 mins with min validation loss 2.43. Training statistics can be accessed on [tfhub.de](https://tensorboard.dev/experiment/RUNPYd1yRgSD5kZSb9hDig/#scalars)
{"language": ["en"], "library_name": "open_clip"}
open_clip en region:us
TAGS
#open_clip #en #region-us 



# Vision-Text dual encoder model training examples

> Note: This example is experimental and might not give the best possible results

The following example showcases how to train a CLIP like vision-text dual encoder model
using a pre-trained vision and text encoder using the JAX/Flax backend.

Such a model can be used for natural language image search and potentially zero-shot image classification.
The model is inspired by the CLIP approach, introduced by Alec Radford et al.
The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their
captions into the same embedding space, such that the caption embeddings are located near the embeddings
of the images they describe.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are immutable and updated in a purely functional
way which enables simple and efficient model parallelism.

In this example we will use the vision model from CLIP
as the image encoder and 'roberta-base' as the text encoder.
Note that one can also use the ViT model as image encoder and any other BERT or ROBERTa model as text encoder.
To train the model on languages other than English one should choose a text encoder trained on the desired
language and a image-text dataset in that language. One such dataset is WIT.	

Let's start by creating a model repository to save the trained model and logs.
Here we call the model '"clip-roberta-base"', but you can change the model name as you like.

You can do this either directly on URL (assuming that
you are logged in) or via the command line:


Next we clone the model repository to add the tokenizer and model files.

To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.



Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the 'run_hybrid_clip.py'.



## How to use the 'FlaxHybridCLIP' model:

The 'FlaxHybridCLIP' class let's you load any text and vision encoder model to create a dual encoder. 
Here is an example of how to load the model using pre-trained text and vision models.



If the checkpoints are in PyTorch then one could pass 'text_from_pt=True' and 'vision_from_pt=True'. This will load the model
PyTorch checkpoints convert them to flax and load the model.



This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
loaded using the pre-trained weights.

## Prepare the dataset

We will use the MS-COCO dataset to train our dual encoder model. MS-COCO contains over 82,000 images, each of which has at least 5 different caption annotations. The dataset is usually used for image captioning tasks, but we can repurpose the image-caption pairs to train our dual encoder model for image search.

### Download and extract the data.

It consists of two compressed folders: one with images, and the other—with associated image captions. Note that the compressed images folder is 13GB in size.



### Prepare dataset files and split the dataset.



> Note: The data loading and processing part of this script can still be improved for maximum performance. In particular one should decode the images beforehand and use those instead decoding them each time. If the dataset is small or if you have huge disk space the you could also pre-process all the dataset beforehand and then use it.

## Train the model
Next we can run the example script to train the model:



This should finish in ~1h50 mins with min validation loss 2.43. Training statistics can be accessed on URL
# Vision-Text dual encoder model training examples

> Note: This example is experimental and might not give the best possible results

The following example showcases how to train a CLIP like vision-text dual encoder model
using a pre-trained vision and text encoder using the JAX/Flax backend.

Such a model can be used for natural language image search and potentially zero-shot image classification.
The model is inspired by the CLIP approach, introduced by Alec Radford et al.
The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their
captions into the same embedding space, such that the caption embeddings are located near the embeddings
of the images they describe.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are immutable and updated in a purely functional
way which enables simple and efficient model parallelism.

In this example we will use the vision model from CLIP
as the image encoder and 'roberta-base' as the text encoder.
Note that one can also use the ViT model as image encoder and any other BERT or ROBERTa model as text encoder.
To train the model on languages other than English one should choose a text encoder trained on the desired
language and a image-text dataset in that language. One such dataset is WIT.	

Let's start by creating a model repository to save the trained model and logs.
Here we call the model '"clip-roberta-base"', but you can change the model name as you like.

You can do this either directly on URL (assuming that
you are logged in) or via the command line:


Next we clone the model repository to add the tokenizer and model files.

To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.



Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the 'run_hybrid_clip.py'. ## How to use the 'FlaxHybridCLIP' model:

The 'FlaxHybridCLIP' class let's you load any text and vision encoder model to create a dual encoder. 
Here is an example of how to load the model using pre-trained text and vision models.



If the checkpoints are in PyTorch then one could pass 'text_from_pt=True' and 'vision_from_pt=True'. This will load the model
PyTorch checkpoints convert them to flax and load the model.



This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
loaded using the pre-trained weights. ## Prepare the dataset

We will use the MS-COCO dataset to train our dual encoder model. MS-COCO contains over 82,000 images, each of which has at least 5 different caption annotations. The dataset is usually used for image captioning tasks, but we can repurpose the image-caption pairs to train our dual encoder model for image search. ### Download and extract the data.

It consists of two compressed folders: one with images, and the other—with associated image captions. Note that the compressed images folder is 13GB in size. ### Prepare dataset files and split the dataset.



> Note: The data loading and processing part of this script can still be improved for maximum performance. In particular one should decode the images beforehand and use those instead decoding them each time. If the dataset is small or if you have huge disk space the you could also pre-process all the dataset beforehand and then use it. ## Train the model
Next we can run the example script to train the model:



This should finish in ~1h50 mins with min validation loss 2.43. Training statistics can be accessed on URL
TAGS
#open_clip #en #region-us 
 # Vision-Text dual encoder model training examples

> Note: This example is experimental and might not give the best possible results

The following example showcases how to train a CLIP like vision-text dual encoder model
using a pre-trained vision and text encoder using the JAX/Flax backend.

Such a model can be used for natural language image search and potentially zero-shot image classification.
The model is inspired by the CLIP approach, introduced by Alec Radford et al.
The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their
captions into the same embedding space, such that the caption embeddings are located near the embeddings
of the images they describe.

JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are immutable and updated in a purely functional
way which enables simple and efficient model parallelism.

In this example we will use the vision model from CLIP
as the image encoder and 'roberta-base' as the text encoder.
Note that one can also use the ViT model as image encoder and any other BERT or ROBERTa model as text encoder.
To train the model on languages other than English one should choose a text encoder trained on the desired
language and a image-text dataset in that language. One such dataset is WIT.	

Let's start by creating a model repository to save the trained model and logs.
Here we call the model '"clip-roberta-base"', but you can change the model name as you like.

You can do this either directly on URL (assuming that
you are logged in) or via the command line:


Next we clone the model repository to add the tokenizer and model files.

To ensure that all tensorboard traces will be uploaded correctly, we need to 
track them. You can run the following command inside your model repo to do so.



Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.

Next, let's add a symbolic link to the 'run_hybrid_clip.py'. ## How to use the 'FlaxHybridCLIP' model:

The 'FlaxHybridCLIP' class let's you load any text and vision encoder model to create a dual encoder. 
Here is an example of how to load the model using pre-trained text and vision models.



If the checkpoints are in PyTorch then one could pass 'text_from_pt=True' and 'vision_from_pt=True'. This will load the model
PyTorch checkpoints convert them to flax and load the model.



This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
loaded using the pre-trained weights. ## Prepare the dataset

We will use the MS-COCO dataset to train our dual encoder model. MS-COCO contains over 82,000 images, each of which has at least 5 different caption annotations. The dataset is usually used for image captioning tasks, but we can repurpose the image-caption pairs to train our dual encoder model for image search. ### Download and extract the data.

It consists of two compressed folders: one with images, and the other—with associated image captions. Note that the compressed images folder is 13GB in size. ### Prepare dataset files and split the dataset.



> Note: The data loading and processing part of this script can still be improved for maximum performance. In particular one should decode the images beforehand and use those instead decoding them each time. If the dataset is small or if you have huge disk space the you could also pre-process all the dataset beforehand and then use it. ## Train the model
Next we can run the example script to train the model:



This should finish in ~1h50 mins with min validation loss 2.43. Training statistics can be accessed on URL