Comments (1)
Hi, I'm glad you're interested in our work.
Image_features
contain the global information of an image, which is the sum of all the local detailed information. To perform anomaly segmentation, specific detailed information at each position in the image is often required, which cannot be directly reflected through image_features
alone.
Therefore, I believe that it is not possible to obtain anomaly maps solely through image_features
and text_features
. However, image_features
do contain certain valuable information. You can consider how to use this information in addition, instead of completely discarding patch_tokens
.
from vand-april-gan.
Related Issues (20)
- 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
- The issue has been resolved.
- Question about License Information
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