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cristianglezm/
ViT-GPT2-FlowerCaptioner-ONNX

Name: ViT-GPT2-FlowerCaptioner-ONNX
Availability: InStock

transformers.js

onnx

vision-encoder-decoder

image-to-text

image-captioning

cristianglezm/FlowerEvolver-Dataset

nlpconnect/vit-gpt2-image-captioning

quantized

apache-2.0

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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

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

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