--- license: mit language: - pt tags: - CAPIVARA - Portuguese CLIP - OpenCLIP datasets: - conceptual_captions - PraCegoVer - MS_COCO - Flickr30K - ImageNet - ELEVATER --- # Model Card for CAPIVARA CAPIVARA is a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. This model holds the state of the art in many zero-shot tasks involving images and Portuguese texts. ## How to use ```python from models.open_clip_wrapper import OpenCLIPWrapper from utils.capivara_utils import download_pretrained_from_hf model_path = download_pretrained_from_hf(model_id="hiaac-nlp/CAPIVARA") model = OpenCLIPWrapper.load_from_checkpoint(model_path, strict=False).model ``` For more details refer to [Github repo](https://github.com/hiaac-nlp/CAPIVARA/). ## Model Details ### Model Description CAPIVARA is built upon pre-trained [OpenCLIP ViT-B/32 XLM-Roberta Base](https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k) and fine-tuned with [Conceptual Captions](https://aclanthology.org/P18-1238.pdf) and synthetic captions generated by [BLIP2](https://huggingface.co/Salesforce/blip2-opt-2.7b-coco). All the captions are translated with Google Translator. ## Uses ### Direct Use Zero-shot image classification, zero-shot image and text retrieval, etc. ### Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image captioning, image generation guiding and conditioning, etc. ## Ethical considerations For ethical considerations, please, refer to the Model Cards section in the [paper](https://arxiv.org/abs/2310.13683). ## Training Details ### Training Data The model was fine-tuned with [Conceptual Captions](https://aclanthology.org/P18-1238.pdf) and synthetic captions generated by [BLIP2](https://huggingface.co/Salesforce/blip2-opt-2.7b-coco). All the captions are translated with Google Translator. #### Training Hyperparameters ``` Optimizer: "Adam" eps: 1e-8 weight_decay: 0.2 betas: [ 0.9, 0.98 ] LR_scheduler: "CosineWarmupLR" min_learning_rate: 1e-7 max_learning_rate: 5e-7 warmup_lr: 500 batch_size: 2816 max_steps: 5863 # 10 epochs ``` ## Evaluation + [Zero-shot image classification](https://github.com/hiaac-nlp/CAPIVARA/blob/main/clip_pt/src/evaluate/capivara_classification.ipynb) + [Zero-shot cross-modal retrieval](https://github.com/hiaac-nlp/CAPIVARA/blob/main/clip_pt/src/evaluate/capivara_retrieval.ipynb) ### Testing Data, Factors & Metrics #### Testing Data For cross-modal retrieval, we used [PraCegoVer](https://www.mdpi.com/2306-5729/7/2/13), which is composed of images annotated originally with Portuguese texts, and our Portuguese-translated versions of [MS COCO](https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48) and [Flickr30k](https://openaccess.thecvf.com/content_iccv_2015/papers/Plummer_Flickr30k_Entities_Collecting_ICCV_2015_paper.pdf). We also translate the labels from [ImageNet](https://ieeexplore.ieee.org/document/5206848) and the [ELEVATER](https://proceedings.neurips.cc/paper_files/paper/2022/hash/3c4688b6a76f25f2311daa0d75a58f1a-Abstract-Datasets_and_Benchmarks.html) benchmark datasets for image classification. ### Results #### Zero-shot Cross-Modal Retrieval We conducted zero-shot cross-modal retrieval experiments on Flickr30k and MS COCO with captions translated into Portuguese, and PraCegoVer. We report the average and standard deviation for 3 runs.
Models Flickr30k MS COCO PraCegoVer
text-to-image image-to-text text-to-image image-to-text text-to-image image-to-text
OpenCLIP ViT-B/32 XLM-Roberta Base (Baseline) 76.23 87.93 52.62 66.55 65.36 69.43
CAPIVARA 79.56 ± 0.01 89.95 ± 0.04 56.27 ± 0.01 71.24 ± 0.01 66.40 ± 0.01 64.75 ± 0.01
#### Zero-shot image classification | Models | **Caltech-101** | **CIFAR-10** | **CIFAR-100** | **Country-211** | **DTD** | **EuroSAT** | **FER-2013** | **FGVC-Aircraft** | **Food-101** | **GTSRB** | **Hateful-Memes** | **KITTI-Distance** | **MNIST** | **Oxford Flowers-102** | **Oxford-IIIT Pets** | **PatchCamelyon** | **Rendered-SST2** | **RESISC-45** | **Stanford-Cars** | **PASCAL VOC-2007** | **Average** | **ImageNet-1k** | |:-----------------------:|:---------------:|:------------:|:-------------:|:---------------:|:------------:|:------------:|:------------:|:-----------------:|:------------:|:------------:|:-----------------:|:------------------:|:------------:|:----------------------:|:--------------------:|:-----------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:------------:|:---------------:| | OpenCLIP ViT-B/32 XLM-Roberta Base (Baseline) | 84.53 ± 0.00 | 93.99 ± 0.00 | 68.44 ± 0.00 | 17.82 ± 0.00 | 41.17 ± 0.00 | 47.16 ± 0.00 | 48.65 ± 0.00 | 26.30 ± 0.00 | 65.06 ± 0.00 | 43.27 ± 0.00 | 56.50 ± 0.00 | 28.41 ± 0.00 | 54.99 ± 0.00 | 50.88 ± 0.00 | 81.56 ± 0.00 | 50.96 ± 0.00 | 54.20 ± 0.00 | 58.51 ± 0.00 | 84.93 ± 0.00 | 82.09 ± 0.00 | 56.97 ± 0.00 | 45.84 ± 0.00 | | CAPIVARA | 82.97 ± 0.03 | 93.85 ± 0.00 | 69.37 ± 0.01 | 17.61 ± 0.00 | 42.34 ± 0.04 | 47.77 ± 0.02 | 46.68 ± 0.05 | 25.49 ± 0.01 | 64.58 ± 0.01 | 46.34 ± 0.01 | 56.17 ± 0.00 | 33.94 ± 0.13 | 60.14 ± 0.04 | 49.93 ± 0.02 | 79.37 ± 0.00 | 51.71 ± 0.01 | 54.82 ± 0.03 | 59.71 ± 0.01 | 85.10 ± 0.02 | 82.29 ± 0.00 | **57.51 ± 0.02** | **46.06 ± 0.01** | ## Environmental Impact - **GPU:** 1 x Quadro RTX 8000 (48 GB) - **Hours used:** 31 hours - **Compute Region:** Brazil - **Carbon footprint:** 0.5 Kg - **Energy**: 6.49 kW ## Citation ```bibtex @inproceedings{santos2023capivara, title={CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages}, author={Santos, Gabriel O. dos and Moreira, Diego A. B. and Ferreira, Alef I. and Silva, Jhessica and Pereira, Luiz and Bueno, Pedro and Sousa, Thiago and Maia, Helena and da Silva, N{\'a}dia and Colombini, Esther and Pedrini, Helio and Avila, Sandra}, booktitle = "Workshop on Multi-lingual Representation Learning (MRL), Conference on Empirical Methods in Natural Language Processing (EMNLP)", year = "2023" } ```