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View Code? Open in Web Editor NEW(CVPR 2023) Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
(CVPR 2023) Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Hi, I have read your paper which is a great work. But I have a question, how is the performance of fine-tuning the entire ResNet50 (both encoder and classifier) on these target fine-grained datasetes with supervised learning? Do you have the result?
Hello, thanks for the great work!
Just wanted to check something:
So when doing SSL and then evaluating just on aircraft for example (the 46.56% value), you use all 10k samples in SSL, and in table 2 you report the accuracy on the training set after training the linear classifier? Thanks in advance!
Hi, thanks for sharing this impressive work! I would like to know what is the specific GPU you used in experiments, like 3090 or v100? And how long it takes to finish the pre train process on X and OS+X?
Hi, I have read your paper repeatedly and that is a good job. At present, I am trying to replicate your results. However, I have encountered some problems in the process of replicating your results, which are as follows:
Following your experimental settings, I first conducted ssl training for Aircraft(train) by using simclr method. Then, test its performance on X(Aircraft test) using run_selfup.sh. However, the test results on X(Aircraft) are only [20.55, 28.32, 90.25](Acc@1, Acc@5, Train Acc).
There is a clear difference between this result and the paper.
Could you possibly give some suggestions as to the cause of this gap?
The specific training parameters I use are as follows:
TAG="aircraft_vanilla"
DATA="aircraft"
CUDA_VISIBLE_DEVICES=0,1,2,3 python train_selfsup.py --tag $TAG \
--no_sampling \
--model resnet50 \
--batch_size 512 \
--precision \
--dataset $DATA \
--data_folder ./data \
--method simclr \
--epochs 5000 \
--cosine \
--learning_rate 1e-1 \
--weight_decay 1e-4
Hi, I have read your paper and that is a great work. But I faced some problems when pretraining on fine-grained datasets. I chose BYOL with resnet50 and Cars datasets. I set batchsize=2048, epoch=1000, cosine lr schedule with lr=1e-1, and weight decay=1e-4. The loss has decayed to 0.1 but the linear evaluation (fixed backbone) result is far away from 50%. I am confused and hope to know what're suitable settings, or have I missed the experiments' details?
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