Comments (5)
Hello, I'll update the code as soon as possible to support ResNet50. I will reply to you immediately once the update is completed. :)
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The bug has been fixed. I just forgot to modify the few-shot part. Thank you for pointing it out!
from vand-april-gan.
Thank you so much for your (fast) help! Everything is working as expected now :)
from vand-april-gan.
Hi, the code has been updated. I highly encourage you to use the new version instead of just replacing modified_resnet.py. You can run with a Resnet50 backbone with this command:
python train.py --dataset mvtec --train_data_path ./data/mvtec \
--save_path ./exps/visa/RN50x16 --config_path ./open_clip/model_configs/RN50x16.json --model RN50x16 \
--features_list 1 2 3 --pretrained openai --image_size 384 --batch_size 8 --aug_rate 0.2 --print_freq 1 \
--epoch 5 --save_freq 1 --learning_rate 0.0001
Note that the parameter features_list
here represents the output features of the block to use, whereas in ViT, this parameter refers to the output features of which layer to use.
from vand-april-gan.
Hi! Thank you for your fast response!
Training is now working, but when I run the test, I got the following error:
Traceback (most recent call last):
File "/code/VAND-APRIL-GAN/test.py", line 308, in <module>
test(args)
File "/code/VAND-APRIL-GAN/test.py", line 183, in test
cos = pairwise.cosine_similarity(normal_features_ls[cls_name[0]][layer].cpu(),
File "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/pairwise.py", line 1251, in cosine_similarity
X, Y = check_pairwise_arrays(X, Y)
File "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/pairwise.py", line 156, in check_pairwise_arrays
X = check_array(
File "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py", line 794, in check_array
raise ValueError(
ValueError: Found array with dim 3. check_pairwise_arrays expected <= 2.
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Related Issues (20)
- Can it work without the mask images? HOT 7
- why the AUPRO lower than the WinCLIP? HOT 1
- Few Shot is just One Shot here? HOT 2
- Issues Recreating Model HOT 2
- -
- Calculation False Positive Rate
- Guidance on Threshold Setting for Accurate Defect Detection in Heatmap Visualizations (red mark) HOT 1
- Can we test without loading pre-trained weights? HOT 4
- Is there a way to get the score of how anomalous an image is? HOT 2
- is that possible to convert and using onnx file? HOT 1
- mvtec epoch HOT 1
- What type of GPU are you using and how long does it take HOT 2
- About text_probs. HOT 1
- 关于训练时梯度的问题 HOT 4
- initialized by other weight HOT 2
- 除了添加的线性层 clip其它部分的权重有被微调吗 HOT 2
- 关于train.py中的的Dataset问题 HOT 1
- global image representations HOT 4
- 检测结果 HOT 1
- few-shot的异常分类分数
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