PQCL

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ICLR 2023 and ICML 2023 paper

PQCL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Code of ICLR paper [ADCLR](https://openreview.net/pdf?id=10R_bcjFwJ) and ICML paper [PQCL](https://sherrylone.github.io/assets/ICML23_PQCL.pdf)

![](https://github.com/Sherrylone/sherrylone.github.io/blob/main/images/ICML23_PQCL.png)

The two papers propose query-based contrastive learning. For ADCLR, we use query crop with pixel information to learn spatial-sensitive information. For PQCL, we further mask the pixel information of the query crops, and add the relative positional embeddings to reconstruct pixel informations. Under the same setting, PQCL can get higher accuracy than ADCLR.

To pretrain the model, run:
```
python3 -m torch.distributed.launch --nproc_per_node=8 \
        --node_rank 0 \
        --master_addr=${MASTER_ADDR:-127.0.0.1} \
        --master_port=${MASTER_PORT:-46123} \
        ./main_pos_bot.py \
        --arch pos_small \
        --output_dir ./output_100eps_005/ \
        --data_path /data/imagenet-1k/train/ \
        --world_size 8 \
        --local_crops_number 1 \
        --local_crops_scale 0.05 0.25 \
        --global_crops_scale 0.25 1 \
        --pred_ratio 0 0.3 \
        --norm_last_layer false \
        --shared_head true \
        --pred_ratio_var 0 0.2 \
        --lambda3 1.0 \
        --batch_size_per_gpu 64 \
        --lambda2 1.0 \
        --epochs 100 \
        --warmup_teacher_temp_epochs 30 \
        --teacher_query_temp 0.07 \
        --teacher_temp 0.07 \
        --local_crops_size 96
```
You can change the vit_small to vit_base to learn ViT-B.

After getting the pretrained model, for linear probing, run:
```
python3 -m torch.distributed.launch --nproc_per_node=8 \
        --master_port=${MASTER_PORT:-36124} \
        ./evaluation/eval_linear.py \
            --pretrained_weights ./output_400eps_base/checkpoint.pth --n_last_blocks 4 \
            --avgpool_patchtokens 0     --arch pos_base     --checkpoint_key teacher \
            --output_dir ./evaluation/output_cls/ \
            --dist_url tcp://localhost:23142 \
            --data_path /data/imagenet-1k/
```

For detection and segmentation on COCO, run:
```
python3 -m torch.distributed.launch --nproc_per_node=8 \
                ./evaluation/object_detection/train.py \
                ./evaluation/object_detection/configs/cascade_rcnn/vit_small_giou_4conv1f_coco_1x.py \
                --launcher pytorch \
                --work-dir ./evaluation/output_detection/ \
                --deterministic \
                --cfg-options model.backbone.use_checkpoint=True \
                model.pretrained=/mnt/workspace/workgroup/shaofeng.zhang/checkpoint/pqcl_small_300eps.pth
```

PQCL

PQCL 최신버젼 다운로드

오늘의 추천 프로젝트

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PQCL

PQCL 최신버젼 다운로드

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