# Problem Image classification or object detection task with yolo when model has good predictive ability on test set with transform.normalize but not working on video camera

**URL:** <https://discuss.pytorch.org/t/problem-image-classification-or-object-detection-task-with-yolo-when-model-has-good-predictive-ability-on-test-set-with-transform-normalize-but-not-working-on-video-camera/178465>\
**Category:** vision\
**Created:** [April 25, 2023, 9:45am UTC](https://discuss.pytorch.org/t/problem-image-classification-or-object-detection-task-with-yolo-when-model-has-good-predictive-ability-on-test-set-with-transform-normalize-but-not-working-on-video-camera/178465 "2023-04-25T09:45:57Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![LouisBao](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/l/7ea924/32.png) [@LouisBao](https://discuss.pytorch.org/u/LouisBao)\
**Post date:** [April 25, 2023, 9:45am UTC](https://discuss.pytorch.org/t/problem-image-classification-or-object-detection-task-with-yolo-when-model-has-good-predictive-ability-on-test-set-with-transform-normalize-but-not-working-on-video-camera/178465/1 "2023-04-25T09:45:57Z")

</div>

- After training my CNN or Resnet50 cutom, I have good predictive performance on test set when I split my dataset into training/val/test set. However, I also want to get the same exact prediction on camera video or on ligne but I have poor prediction ability, all objects are unclassifiable. When I test my transform for each frame of the video or small image of each frame, I get a black image or an image that I don’t want.

- If I don’t use transforms.normalize to augment the data, I’ve got instability during training, which is very difficult to fix. If I test this trained model on camera, I get the same result, poor predictability.

- I don’t know how to deal with this problem, but if the image dataset and the real camera image are very different, does my model have a bad prediction, it’s normal? or How to get mean and std on my dataset is good for camera image?

- My transforms for data augmentation:

```auto
train_transform = transforms.Compose([
                                    transforms.Resize((img_size, img_size)),
                                    transforms.Pad(padding=30),
                                    transforms.GaussianBlur(kernel_size=(5,9),sigma=(0.1,0.5)),
                                    transforms.RandomHorizontalFlip(p=0.5),
                                    transforms.RandomVerticalFlip(p=0.5),
                                    transforms.RandomPerspective(p=0.5),
                                    transforms.RandomAffine(degrees=(30,70)),
                                    transforms.ToTensor(),
                                    transforms.Normalize(
                                        mean=[0.58067408, 0.5785063, 0.5922596],
                                        std=[0.33043968, 0.33169742, 0.33235405])])

```

- My transform for test model:

```auto
test_transform = transforms.Compose([
                                    transforms.Resize((img_size, img_size)),
                                    transforms.ToTensor(),
                                    transforms.Normalize(
                                        mean=[0.58067408, 0.5785063, 0.5922596],
                                        std=[0.33043968, 0.33169742, 0.33235405])])

```