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