ViT-GPT2-FlowerCaptioner
This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the FlowerEvolver-dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.4930
- Rouge1: 68.3498
- Rouge2: 46.7534
- Rougel: 62.3763
- Rougelsum: 65.9575
- Gen Len: 49.82
sample running code
with python
from transformers import pipeline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
FlowerCaptioner = pipeline("image-to-text", model="cristianglezm/ViT-GPT2-FlowerCaptioner", device=device)
FlowerCaptioner(["flower1.png"])
# A flower with 12 petals in a smooth gradient of green and blue.
# The center is green with black accents. The stem is long and green.
with javascript
import { pipeline } from '@xenova/transformers';
// Allocate a pipeline for image-to-text
let pipe = await pipeline('image-to-text', 'cristianglezm/ViT-GPT2-FlowerCaptioner-ONNX');
let out = await pipe('flower image url');
// A flower with 12 petals in a smooth gradient of green and blue.
// The center is green with black accents. The stem is long and green.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
---|---|---|---|---|---|---|---|---|
0.6986 | 1.0 | 100 | 0.5339 | 64.9813 | 42.4686 | 58.2586 | 63.3933 | 47.25 |
0.3408 | 2.0 | 200 | 0.3263 | 67.5461 | 46.5219 | 62.7962 | 65.6509 | 47.39 |
0.2797 | 3.0 | 300 | 0.2829 | 65.0704 | 42.0682 | 58.4268 | 63.2368 | 56.8 |
0.2584 | 4.0 | 400 | 0.2588 | 65.5074 | 45.227 | 60.2469 | 63.4253 | 52.25 |
0.2589 | 5.0 | 500 | 0.2607 | 66.7346 | 45.8264 | 61.7373 | 64.8857 | 50.64 |
0.2179 | 6.0 | 600 | 0.2697 | 63.8334 | 42.997 | 58.1585 | 61.7704 | 52.43 |
0.1662 | 7.0 | 700 | 0.2631 | 68.6188 | 48.3329 | 63.9474 | 66.6006 | 46.94 |
0.161 | 8.0 | 800 | 0.2749 | 69.0046 | 48.1421 | 63.7844 | 66.8317 | 49.74 |
0.1207 | 9.0 | 900 | 0.3117 | 70.0357 | 48.9002 | 64.416 | 67.7582 | 48.66 |
0.0909 | 10.0 | 1000 | 0.3408 | 65.9578 | 45.2324 | 60.2838 | 63.7493 | 46.92 |
0.0749 | 11.0 | 1100 | 0.3516 | 67.4244 | 46.1985 | 61.6408 | 65.5371 | 46.61 |
0.0665 | 12.0 | 1200 | 0.3730 | 68.6911 | 47.7089 | 63.0381 | 66.6956 | 47.89 |
0.0522 | 13.0 | 1300 | 0.3891 | 67.2365 | 45.4165 | 61.4063 | 64.857 | 48.91 |
0.0355 | 14.0 | 1400 | 0.4128 | 69.1494 | 47.9278 | 63.3334 | 66.5969 | 50.55 |
0.0309 | 15.0 | 1500 | 0.4221 | 66.2447 | 44.937 | 60.1403 | 63.8541 | 50.71 |
0.0265 | 16.0 | 1600 | 0.4343 | 67.8178 | 46.7084 | 61.8173 | 65.4375 | 50.85 |
0.0158 | 17.0 | 1700 | 0.4577 | 67.9846 | 45.9562 | 61.6353 | 65.7207 | 50.81 |
0.0166 | 18.0 | 1800 | 0.4731 | 69.0971 | 47.7001 | 62.856 | 66.7796 | 50.01 |
0.0121 | 19.0 | 1900 | 0.4657 | 68.1397 | 46.4258 | 62.2696 | 65.9332 | 49.15 |
0.0095 | 20.0 | 2000 | 0.4793 | 68.6497 | 47.9446 | 63.0466 | 66.5409 | 50.96 |
0.0086 | 21.0 | 2100 | 0.4780 | 68.4363 | 46.7296 | 62.359 | 66.2626 | 50.02 |
0.0068 | 22.0 | 2200 | 0.4863 | 67.5415 | 46.0821 | 61.57 | 65.4613 | 49.5 |
0.0061 | 23.0 | 2300 | 0.4892 | 68.1283 | 46.5802 | 62.0832 | 66.0203 | 50.21 |
0.006 | 24.0 | 2400 | 0.4912 | 68.1723 | 46.3239 | 62.2007 | 65.6725 | 49.89 |
0.0057 | 25.0 | 2500 | 0.4930 | 68.3498 | 46.7534 | 62.3763 | 65.9575 | 49.82 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.4.1+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
nlpconnect/vit-gpt2-image-captioning