Comments (5)
Hi! What's your intuition on why auroc_sp is much lower on image vs pixel? And what could be done to improve?
Hi! In the zero-shot setting, the anomaly scores used for classification are obtained by calculating the similarity between class tokens and text features. In this case, the accuracy of classification and segmentation is not significantly relevant. The accuracy of classification primarily depends on the pre-trained model and the prompts. Designing appropriate prompts manually is quite challenging. Perhaps you could try prompt learning to further enhance the classification performance.
Besides, due to the sufficiently high predictive accuracy of anomaly maps, you can also attempt to take their maximum values as anomaly scores. Adding these two parts together can further enhance performance.
from vand-april-gan.
Hi, I'm sorry for replying late.
In our approach, we focused on exploring the CLIP model based on ViTs without conducting extensive experiments on the model based on ResNets. I can provide you with a reference result obtained by training the RN50x16
model for 5 epochs at a resolution of 384 (the standard resolution for this model).
objects | auroc_px | f1_px | ap_px | aupro | auroc_sp | f1_sp | ap_sp |
---|---|---|---|---|---|---|---|
candle | 97.5 | 16.9 | 9.9 | 87.1 | 94.4 | 87.5 | 93.9 |
capsules | 90.4 | 8.0 | 3.6 | 67.9 | 62.2 | 77.2 | 73.1 |
cashew | 82.0 | 12.8 | 8.1 | 86.2 | 60.7 | 80.2 | 79.3 |
chewinggum | 99.2 | 70.8 | 75.9 | 82.7 | 94.9 | 94.5 | 97.9 |
fryum | 92.8 | 25.0 | 18.1 | 79.0 | 77.5 | 81.8 | 87.9 |
macaroni1 | 97.3 | 24.1 | 13.3 | 87.3 | 43.5 | 66.7 | 48.0 |
macaroni2 | 96.7 | 9.5 | 3.1 | 86.3 | 52.6 | 66.9 | 51.2 |
pcb1 | 90.6 | 7.8 | 4.5 | 81.1 | 71.6 | 70.8 | 69.1 |
pcb2 | 89.0 | 14.2 | 6.3 | 68.6 | 61.0 | 67.4 | 63.0 |
pcb3 | 88.1 | 9.3 | 4.6 | 66.0 | 65.2 | 68.1 | 65.9 |
pcb4 | 94.1 | 19.9 | 13.4 | 81.8 | 94.6 | 89.9 | 94.8 |
pipe_fryum | 96.5 | 35.3 | 24.9 | 94.5 | 92.1 | 90.7 | 96.1 |
mean | 92.8 | 21.1 | 15.5 | 80.7 | 72.5 | 78.5 | 76.7 |
The training command is:
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 4 --pretrained openai --image_size 384 --batch_size 8 --aug_rate 0.2 --print_freq 1 \
--epoch 5 --save_freq 1 --learning_rate 0.0001
In our experiments, we found that training the linear layers with ResNets is more challenging compared to using ViTs, and it may require more suitable hyperparameters and training strategies. The code now supports changing resolutions, so you can continue your exploration using these code modifications.
Hope that my answer can be helpful to you. :)
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Hi! What's your intuition on why auroc_sp is much lower on image vs pixel? And what could be done to improve?
from vand-april-gan.
Do you have any reference paper for prompt learning?
from vand-april-gan.
Do you have any reference paper for prompt learning?
I'm sorry, I just noticed this question. You can refer to CoOp and AnomalyCLIP.
from vand-april-gan.
Related Issues (20)
- 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
- Show visualize heatmap
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