CLIP-Decoder:Multilabel Classification in ZSL settings

[Paper][(https://arxiv.org/abs/2406.14830) | Pretrained Models | Datasets

Official PyTorch Implementation

Muhammad Ali, Dr. Salman Khan
MBZUAI

Abstract

Multi-label classification is an essential task utilized in a wide variety of real-world applications. Multi-label zero-shot learning is a method for classifying images into multiple unseen categories for which no training data is available, while in general zero-shot situations, the test set may include observed classes. The CLIP-Decoder is a novel method based on the state-of-the-art ML-Decoder attention-based head. We introduce multi-modal representation learning in CLIP-Decoder, utilizing the text encoder to extract text features and the image encoder for image feature extraction. Furthermore, we minimize semantic mismatch by aligning image and word embeddings in the same dimension and comparing their respective representations using a combined loss, which comprises classification loss and CLIP loss. This strategy outperforms other methods and we achieve cutting-edge results on zero-shot multilabel classification tasks using CLIP-Decoder. Our method achieves an absolute increase of 3.9% in performance compared to existing methods for zero-shot learning multi-label classification tasks. Additionally, in the generalized zero-shot learning multi-label classification task, our method shows an impressive increase of almost 2.3%.

ML-Decoder Implementation

ML-Decoder implementation is available here. It can be easily integrated into any backbone using this example code:

ml_decoder_head = MLDecoder(num_classes) # initilization

spatial_embeddings = self.backbone(input_image) # backbone generates spatial embeddings      
 
logits = ml_decoder_head(spatial_embeddings) # transfrom spatial embeddings to logits

Inference Code and Pretrained Models

See Model Zoo

Training Code

We share a full reproduction code for the article results.

Multi-label Training Code

A reproduction code for MS-COCO multi-label:

python train.py  \
--data=/home/datasets/coco2014/ \
--model_name=tresnet_l \
--image_size=448

Single-label Training Code

Our single-label training code uses the excellent timm repo. Reproduction code is currently from a fork, we will work toward a full merge to the main repo.

git clone https://github.com/mrT23/pytorch-image-models.git

This is the code for A2 configuration training, with ML-Decoder (--use-ml-decoder-head=1):

python -u -m torch.distributed.launch --nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
./train.py \
/data/imagenet/ \
--amp \
-b=256 \
--epochs=300 \
--drop-path=0.05 \
--opt=lamb \
--weight-decay=0.02 \
--sched='cosine' \
--lr=4e-3 \
--warmup-epochs=5 \
--model=resnet50 \
--aa=rand-m7-mstd0.5-inc1 \
--reprob=0.0 \
--remode='pixel' \
--mixup=0.1 \
--cutmix=1.0 \
--aug-repeats 3 \
--bce-target-thresh 0.2 \
--smoothing=0 \
--bce-loss \
--train-interpolation=bicubic \
--use-ml-decoder-head=1

ZSL Training Code

First download the following files to the root path of the dataset:

benchmark_81_v0.json
wordvec_array.pth
data.csv

Training code for NUS-WIDE ZSL:

python train_zsl_nus.py  \
--data=/home/datasets/nus_wide/ \
--image_size=224

New Top Results - Stanford-Cars and CIFAR 100

Using ML-Decoder classification head, we reached a top result of 96.41% on Stanford-Cars dataset, and 95.1% on CIFAR-100 dataset. We will add this result to a future version of the paper.

Citation

@misc{ridnik2021mldecoder,
      title={ML-Decoder: Scalable and Versatile Classification Head}, 
      author={Tal Ridnik and Gilad Sharir and Avi Ben-Cohen and Emanuel Ben-Baruch and Asaf Noy},
      year={2021},
      eprint={2111.12933},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}