cristianglezm commited on
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upload model

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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - image-to-text
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+ - image-captioning
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+ license: apache-2.0
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+ base_model: nlpconnect/vit-gpt2-image-captioning
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+ widget:
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+ - src: >-
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+ https://huggingface.co/datasets/cristianglezm/FlowerEvolver-Dataset/resolve/main/flowers/001.png
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+ example_title: Flower 1
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+ - src: >-
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+ https://huggingface.co/datasets/cristianglezm/FlowerEvolver-Dataset/resolve/main/flowers/002.png
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+ example_title: Flower 2
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+ - src: >-
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+ https://huggingface.co/datasets/cristianglezm/FlowerEvolver-Dataset/resolve/main/flowers/003.png
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+ example_title: Flower 3
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+ datasets:
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+ - cristianglezm/FlowerEvolver-Dataset
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+ metrics:
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+ - rouge
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+ pipeline_tag: image-to-text
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+ library_name: transformers
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+ ---
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+
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+ # ViT-GPT2-FlowerCaptioner
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+
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+ This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on the [FlowerEvolver-dataset](https://huggingface.co/datasets/cristianglezm/FlowerEvolver-Dataset) dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3075
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+ - Rouge1: 66.3702
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+ - Rouge2: 45.5642
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+ - Rougel: 61.401
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+ - Rougelsum: 64.0587
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+ - Gen Len: 49.97
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+
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+ ## sample running code
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+
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+ with python
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ FlowerCaptioner = pipeline("image-to-text", model="cristianglezm/ViT-GPT2-FlowerCaptioner", device=device)
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+ FlowerCaptioner(["flower1.png"])
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+ # A flower with 12 petals in a smooth gradient of green and blue.
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+ # The center is green with black accents. The stem is long and green.
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+ ```
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+
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+ with javascript
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+
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+ ```javascript
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+ import { pipeline } from '@xenova/transformers';
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+
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+ // Allocate a pipeline for image-to-text
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+ let pipe = await pipeline('image-to-text', 'cristianglezm/ViT-GPT2-FlowerCaptioner-ONNX');
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+
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+ let out = await pipe('flower image url');
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+ // A flower with 12 petals in a smooth gradient of green and blue.
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+ // The center is green with black accents. The stem is long and green.
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+ ```
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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+ - eval_batch_size: 4
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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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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 0.6755 | 1.0 | 100 | 0.5339 | 60.9402 | 39.3331 | 54.6889 | 59.45 | 36.75 |
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+ | 0.3666 | 2.0 | 200 | 0.3331 | 65.5149 | 43.0245 | 59.3121 | 62.7329 | 52.82 |
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+ | 0.2983 | 3.0 | 300 | 0.3075 | 66.3702 | 45.5642 | 61.401 | 64.0587 | 49.97 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.2
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+ - Pytorch 2.4.1+cu124
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+ - Datasets 2.20.0
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+ - Tokenizers 0.13.3
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