Image-caption-generator
This model is trained on 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
Load the pre-trained model from the model hub
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)
Load the image for which the caption is to be generated(note: replace the value of
img_name
with image of your choice)### replace the value with your image img_name = "flickr_data.jpg" img = Image.open(img_name) if img.mode != 'RGB': img = img.convert(mode="RGB")
Pre-process the image
pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values pixel_values = pixel_values.to(device)
Generate the caption
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.
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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