# install python 3.6, torch==1.8.1, torchvision==0.9.1 pip install -r requirements.txt python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/
# install python 3.6, torch==1.8.1, torchvision==0.9.1 pip install -r requirements.txt python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 9' # for fa...
coreset_sampling_ratio)) self.embedding_coreset = total_embeddings[selected_idx] print('initial embedding size : ', total_embeddings.shape) print('final embedding size : ', self.embedding_coreset.shape) with open(os.path.join(self.embedding_dir_path, 'embedding.pickle'), 'wb') as f...
sample embeddings/carpet/embedding.pickle => coreset_sampling_ratio=0.001 updates(21/06/26) : A criticalissuerelated to "locally aware patch" raised and fixed. Score table is updated. Usage # install python 3.6, torch==1.8.1, torchvision==0.9.1 pip install -r requirements.txt python train....
We first adopt the traditional model evaluation method, randomly sampling normal data and abnormal data, and divide the dataset according to the ratio of training set to test set of 8:2. In order to avoid overfitting and other effects caused by unreasonable dataset division, we adopt the cross...
parser.add_argument('--coreset_sampling_ratio', default=0.01) parser.add_argument('--output_path', default=r'./outputs') parser.add_argument('--save_anomaly_map', default=True) parser.add_argument('--n_neighbors', type=int, default=9) args = parser.parse_args() return args if __...