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---
tags:
- image-captioning
- image-to-text
model-index:
- name: image-caption-generator
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Image-caption-generator
This model is trained on [Flickr8k](https://www.kaggle.com/datasets/nunenuh/flickr8k) dataset to generate captions given an image.
It achieves the following results on the evaluation set:
- eval_loss: 0.2536
- eval_runtime: 25.369
- eval_samples_per_second: 63.818
- eval_steps_per_second: 8.002
- epoch: 4.0
- step: 3236
# Running the model using transformers library
1. Load the pre-trained model from the model hub
```python
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image
model_name = "bipin/image-caption-generator"
# load model
model = VisionEncoderDecoderModel.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
```
2. Load the image for which the caption is to be generated
```python
img_name = "flickr_data.jpg"
img = Image.open(img_name)
if img.mode != 'RGB':
img = img.convert(mode="RGB")
```
3. Pre-process the image
```python
pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
```
4. Generate the caption
```python
max_length = 128
num_beams = 4
# get model prediction
output_ids = model.generate(pixel_values, num_beams=num_beams, max_length=max_length)
# decode the generated prediction
preds = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(preds)
```
## Training procedure
The procedure used to train this model can be found [here](https://bipinkrishnan.github.io/ml-recipe-book/image_captioning.html).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
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