# 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...
# 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...
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 __...