$ git clone https://github.com/knjcode/pytorch-finetuner $ cd pytorch-finetuner $ pip install -r requirements.txt A training data directory (images/train), validation data directory (images/valid), and test data directory (images/test) should containing one subdirectory per image class.
For example, arrange training, validation, and test data as follows.
images/ train/ airplanes/ airplane001.jpg airplane002.jpg ... watch/ watch001.jpg watch002.jpg ... valid/ airplanes/ airplane101.jpg airplane102.jpg ... watch/ watch101.jpg watch102.jpg ... test/ airplanes/ airplane201.jpg airplane202.jpg ... watch/ watch201.jpg watch202.jpg ... Download Caltech 101 dataset, and split part of it into the example_images directory.
$ util/caltech101_prepare.sh example_images/trainis train set of 60 images for each classesexample_images/validis validation set of 20 images for each classesexample_imags/testis test set of 20 images for each classes
$ util/counter.sh example_images/train example_images/train contains 10 directories Faces 60 Leopards 60 Motorbikes 60 airplanes 60 bonsai 60 car_side 60 chandelier 60 hawksbill 60 ketch 60 watch 60 With this dataset you can immediately try fine-tuning with pytorch-finetuner.
$ ./train.py example_images --model resnet50 --epochs 30 --lr-step-epochs 10,20 $ ./train.py example_images --epochs 5 $ ./train.py example_images --epochs 5 Running script with args: Namespace(base_lr=0.0125, batch_size=128, cosine_annealing_eta_min=1e-05, cosine_annealing_mult=2, cosine_annealing_t_max=None, cuda=True, cutout=False, cutout_holes=None, cutout_length=None, data='example_images', disp_batches=1, epochs=3, image_dump=False, input_size=224, jitter_brightness=0.1, jitter_contrast=0.1, jitter_hue=0.05, jitter_saturation=0.1, log_dir='logs', lr_factor=0.1, lr_patience=None, lr_step_epochs=[30, 60, 90], mixup=False, mixup_alpha=None, model='resnet18', model_dir='model', momentum=0.9, no_cuda=False, optimizer='sgd', prefix='20181226184319', random_erasing=False, random_erasing_p=None, random_erasing_r1=None, random_erasing_r2=None, random_erasing_sh=None, random_erasing_sl=None, random_horizontal_flip=0.5, random_resized_crop_ratio=[0.75, 1.3333333333333333], random_resized_crop_scale=[0.08, 1.0], random_rotate_degree=3.0, random_vertical_flip=0.0, resume=None, rgb_mean=[0.485, 0.456, 0.406], rgb_std=[0.229, 0.224, 0.225], ricap=False, ricap_beta=None, ricap_with_line=False, save_best_and_last=False, save_best_only=False, scale_size=256, scratch=False, seed=None, start_epoch=0, warmup_epochs=5, wd=0.0001, workers=22) scale_size: 256 input_size: 224 rgb_mean: [0.0, 0.0, 0.0] rgb_std: [1.0, 1.0, 1.0] number of train dataset: 600 number of validation dataset: 200 number of classes: 10 => using pre-trained model 'resnet18' => using GPU => using optimizer: sgd => using MultiStepLR scheduler => model and logs prefix: 20181226184319 => log dir: logs => model dir: model => tensorboardX log dir: logs/20181226184319-tensorboardX Epoch[0] Batch[1] [128/600 (20%)] speed: 15.22 samples/sec accuracy: 0.0781250000 loss: 2.5856597424 Epoch[0] Batch[2] [256/600 (40%)] speed: 30.00 samples/sec accuracy: 0.0820312500 loss: 2.5729613304 Epoch[0] Batch[3] [384/600 (60%)] speed: 44.36 samples/sec accuracy: 0.0885416642 loss: 2.5417649746 Epoch[0] Batch[4] [512/600 (80%)] speed: 58.31 samples/sec accuracy: 0.0996093750 loss: 2.5194582939 Epoch[0] Batch[5] [600/600 (100%)] speed: 60.46 samples/sec accuracy: 0.1183238626 loss: 2.4788246155 Epoch[0] Train-accuracy: 0.11832386255264282 Epoch[0] Train-loss: 2.4788246154785156 Epoch[0] learning-rate: 0.0020833333333333333 Epoch[0] Validation-accuracy: 0.2777777910232544 Epoch[0] Validation-loss: 2.0763139724731445 Epoch[0] Time cost: 13.761763095855713 [sec] => Saved checkpoint to "model/20181226184319-resnet18-0001.model" => Saved checkpoint to "model/20181226184319-resnet18-best.model" Epoch[1] Batch[1] [128/600 (20%)] speed: 42.31 samples/sec accuracy: 0.2578125000 loss: 2.2109849453 Epoch[1] Batch[2] [256/600 (40%)] speed: 81.27 samples/sec accuracy: 0.3007812500 loss: 2.1127164364 ... (snip) ... Epoch[4] Batch[1] [128/600 (20%)] speed: 42.00 samples/sec accuracy: 0.9531250000 loss: 0.2027403414 Epoch[4] Batch[2] [256/600 (40%)] speed: 80.71 samples/sec accuracy: 0.9648437500 loss: 0.1552993804 Epoch[4] Batch[3] [384/600 (60%)] speed: 116.50 samples/sec accuracy: 0.9661458135 loss: 0.1483899951 Epoch[4] Batch[4] [512/600 (80%)] speed: 149.72 samples/sec accuracy: 0.9726562500 loss: 0.1362725645 Epoch[4] Batch[5] [600/600 (100%)] speed: 170.88 samples/sec accuracy: 0.9690340757 loss: 0.1381170452 Epoch[4] Train-accuracy: 0.9690340757369995 Epoch[4] Train-loss: 0.13811704516410828 Epoch[4] learning-rate: 0.010416666666666668 Epoch[4] Validation-accuracy: 1.0 Epoch[4] Validation-loss: 0.016005855053663254 Epoch[4] Time cost: 5.863810300827026 [sec] => Saved checkpoint to "model/20181226184319-resnet18-0005.model" => Saved checkpoint to "model/20181226184319-resnet18-best.model" $ ./test.py example_images -m model/20181226184319-resnet18-best.model $ ./test.py example_images -m model/20181226184319-resnet18-best.model Running script with args: Namespace(batch_size=128, cuda=True, data='example_images', input_size=None, log_dir='logs', model='model/20181226184319-resnet18-best.model', no_cuda=False, num_classes=None, prefix='20190117043959', print_cr=False, rgb_mean='0,0,0', rgb_std='1,1,1', scale_size=None, seed=None, topk=3, tta=False, tta_custom_seven_crop=False, tta_custom_six_crop=False, tta_custom_ten_crop=False, tta_custom_twenty_crop=False, tta_ten_crop=False, workers=22) => loading saved checkpoint 'model/20181226184319-resnet18-best.model' scale_size: 256 input_size: 224 rgb_mean: [0.0, 0.0, 0.0] rgb_std: [1.0, 1.0, 1.0] number of test dataset: 200 number of classes: 10 Test: 100%|██████████| 2/2 [00:06<00:00, 4.37s/it, loss=0.0231, accuracy=99.6] => Saved test results to "logs/20190117043959-test-results.log" => Saved classification report to "logs/20190117043959-test-classification_report.log" model: model/20181226184319-resnet18-best.model Test-loss: 0.023076653480529785 Test-accuracy: 0.995 (199/200) => Saved test log to "logs/20190117043959-test.log" $ ./train.py example_images/train --calc-rgb-mean-and-std --batch-size 600 => Calculate rgb mean and std (dir: example_images/train images: 600 batch-size: 600) Calc rgb mean/std: 100%|████████████████████████| 1/1 [00:04<00:00, 4.19s/it] => processed: 600 images => calculated rgb mean: [0.5068701 0.50441307 0.47790593] => calculated rgb std: [0.28773245 0.27445307 0.29044855] Please use following command options when train and test: --rgb-mean 0.507,0.504,0.478 --rgb-std 0.288,0.274,0.290 ./train.py example_images --random-rotate-degree 3.0 ./train.py example_images \ --random-resized-crop-scale 0.08,1.0 \ --random-resized-crop-ratio 0.75,1.3333333333333333 ./train.py example_images --random-horizontal-flip 0.5 ./train.py example_images --random-vertical-flip 0.5 ./train.py example_images \ --jitter-brightness 0.10 \ --jitter-contrast 0.10 \ --jitter-saturation 0.10 \ --jitter-hue 0.05 ./train.py example_images \ --rgb-mean 0.485,0.456,0.406 \ --rgb-std 0.229,0.224,0.225 # Cutout $ ./train.py example_images --cutout # Random Erasing $ ./train.py example_images --random-erasing # mixup $ ./train.py example_images --mixup # RICAP $ ./train.py example_images --ricap # ICAP $ ./train.py example_images --icap # CutMix $ ./train.py example_images --cutmix # mixup + Cutout $ ./train.py example_images --mixup --cutout # mixup + Random Erasing $ ./train.py example_images --mixup --random-erasing # RICAP + Cutout $ ./train.py example_images --ricap --cutout # RICAP + Random Erasing $ ./train.py example_images --ricap --random-erasing - Cutout: Improved Regularization of Convolutional Neural Networks with Cutout
- Random Erasing: Random Erasing Data Augmentation
- mixup: mixup: Beyond Empirical Risk Minimization
- RICAP: Data Augmentation using Random Image Cropping and Patching for Deep CNNs
- CutMix: CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
usage: train.py [-h] [--model ARCH] [--from-scratch] [--epochs EPOCHS] [--batch-size BATCH_SIZE] [--val-batch-size VAL_BATCH_SIZE] [-j WORKERS] [--prefix PREFIX] [--log-dir LOG_DIR] [--model-dir MODEL_DIR] [--resume MODEL] [--start-epoch START_EPOCH] [--disp-batches DISP_BATCHES] [--save-best-only] [--save-best-and-last] [--drop-last] [--base-lr BASE_LR] [--lr-factor LR_FACTOR] [--lr-step-epochs LR_STEP_EPOCHS] [--lr-patience LR_PATIENCE] [--cosine-annealing-t-max COSINE_ANNEALING_T_MAX] [--cosine-annealing-mult COSINE_ANNEALING_MULT] [--cosine-annealing-eta-min COSINE_ANNEALING_ETA_MIN] [--final-lr FINAL_LR] [--optimizer OPTIMIZER] [--momentum MOMENTUM] [--wd WD] [--warmup-epochs WARMUP_EPOCHS] [--scale-size SCALE_SIZE] [--input-size INPUT_SIZE] [--rgb-mean RGB_MEAN] [--rgb-std RGB_STD] [--random-resized-crop-scale RANDOM_RESIZED_CROP_SCALE] [--random-resized-crop-ratio RANDOM_RESIZED_CROP_RATIO] [--random-horizontal-flip RANDOM_HORIZONTAL_FLIP] [--random-vertical-flip RANDOM_VERTICAL_FLIP] [--jitter-brightness JITTER_BRIGHTNESS] [--jitter-contrast JITTER_CONTRAST] [--jitter-saturation JITTER_SATURATION] [--jitter-hue JITTER_HUE] [--random-rotate-degree RANDOM_ROTATE_DEGREE] [--interpolation {BILINEAR,BICUBIC,NEAREST}] [--image-dump] [--calc-rgb-mean-and-std] [--no-cuda] [--seed SEED] [--warm_restart_next WARM_RESTART_NEXT] [--warm_restart_current WARM_RESTART_CURRENT] [--cutout] [--cutout-holes CUTOUT_HOLES] [--cutout-length CUTOUT_LENGTH] [--random-erasing] [--random-erasing-p RANDOM_ERASING_P] [--random-erasing-sl RANDOM_ERASING_SL] [--random-erasing-sh RANDOM_ERASING_SH] [--random-erasing-r1 RANDOM_ERASING_R1] [--random-erasing-r2 RANDOM_ERASING_R2] [--mixup] [--mixup-alpha MIXUP_ALPHA] [--ricap] [--ricap-beta RICAP_BETA] [--ricap-with-line] [--icap] [--icap-beta ICAP_BETA] [--cutmix] [--cutmix-beta CUTMIX_BETA] [--cutmix-prob CUTMIX_PROB] DIR train positional arguments: DIR path to dataset (train and validation) optional arguments: -h, --help show this help message and exit --model ARCH, -m ARCH specify model architecture (default: resnet18) --from-scratch do not use pre-trained weights (default: False) --epochs EPOCHS number of total epochs to run (default: 30) --batch-size BATCH_SIZE, -b BATCH_SIZE the batch size (default: 128) --val-batch-size VAL_BATCH_SIZE the validation batch size (default: 256) -j WORKERS, --workers WORKERS number of data loading workers (default: 80% of the number of cores) --prefix PREFIX prefix of model and logs (default: auto) --log-dir LOG_DIR log directory (default: logs) --model-dir MODEL_DIR model saving dir (default: model) --resume MODEL path to saved model (default: None) --start-epoch START_EPOCH manual epoch number (default: 0) --disp-batches DISP_BATCHES show progress for every n batches (default: auto) --save-best-only save only the latest best model according to the validation accuracy (default: False) --save-best-and-last save last and latest best model according to the validation accuracy (default: False) --drop-last drop the last incomplete batch, if the dataset size is not divisible by the batch size. (default: False) --base-lr BASE_LR initial learning rate (default: 0.001) --lr-factor LR_FACTOR the ratio to reduce lr on each step (default: 0.1) --lr-step-epochs LR_STEP_EPOCHS the epochs to reduce the lr (default: 10,20) --lr-patience LR_PATIENCE enable ReduceLROnPlateau lr scheduler with specified patience (default: None) --cosine-annealing-t-max COSINE_ANNEALING_T_MAX enable CosineAnnealinigLR scheduler with specified T_max (default: None) --cosine-annealing-mult COSINE_ANNEALING_MULT T_mult of CosineAnnealingLR scheduler --cosine-annealing-eta-min COSINE_ANNEALING_ETA_MIN Minimum learning rate of CosineannealingLR scheduler --final-lr FINAL_LR final_lr of AdaBound optimizer --optimizer OPTIMIZER the optimizer type (default: sgd) --momentum MOMENTUM momentum (default: 0.9) --wd WD weight decay (default: 1e-04) --warmup-epochs WARMUP_EPOCHS number of warmup epochs (default: 5) --scale-size SCALE_SIZE scale size (default: auto) --input-size INPUT_SIZE input size (default: auto) --rgb-mean RGB_MEAN RGB mean (default: 0,0,0) --rgb-std RGB_STD RGB std (default: 1,1,1) --random-resized-crop-scale RANDOM_RESIZED_CROP_SCALE range of size of the origin size cropped (default: 0.08,1.0) --random-resized-crop-ratio RANDOM_RESIZED_CROP_RATIO range of aspect ratio of the origin aspect ratio cropped (defaullt: 0.75,1.3333333333333333) --random-horizontal-flip RANDOM_HORIZONTAL_FLIP probability of the image being flipped (default: 0.5) --random-vertical-flip RANDOM_VERTICAL_FLIP probability of the image being flipped (default: 0.0) --jitter-brightness JITTER_BRIGHTNESS jitter brightness of data augmentation (default: 0.10) --jitter-contrast JITTER_CONTRAST jitter contrast of data augmentation (default: 0.10) --jitter-saturation JITTER_SATURATION jitter saturation of data augmentation (default: 0.10) --jitter-hue JITTER_HUE jitter hue of data augmentation (default: 0.05) --random-rotate-degree RANDOM_ROTATE_DEGREE rotate degree of data augmentation (default: 3.0) --interpolation {BILINEAR,BICUBIC,NEAREST} interpolation. (default: BILINEAR) --image-dump dump batch images and exit (default: False) --calc-rgb-mean-and-std calculate rgb mean and std of train images and exit (default: False) --no-cuda disables CUDA training (default: False) --seed SEED seed for initializing training. (default: None) --warm_restart_next WARM_RESTART_NEXT next warm restart epoch (default: None --warm_restart_current WARM_RESTART_CURRENT current warm restart epoch (default: None) --cutout apply cutout (default: False) --cutout-holes CUTOUT_HOLES number of holes to cut out from image (default: 1) --cutout-length CUTOUT_LENGTH length of the holes (default: 64) --random-erasing apply random erasing (default: False) --random-erasing-p RANDOM_ERASING_P random erasing p (default: 0.5) --random-erasing-sl RANDOM_ERASING_SL random erasing sl (default: 0.02) --random-erasing-sh RANDOM_ERASING_SH random erasing sh (default: 0.4) --random-erasing-r1 RANDOM_ERASING_R1 random erasing r1 (default: 0.3) --random-erasing-r2 RANDOM_ERASING_R2 random erasing r2 (default: 3.3333333333333335) --mixup apply mixup (default: Falsse) --mixup-alpha MIXUP_ALPHA mixup alpha (default: 0.2) --ricap apply RICAP (default: False) --ricap-beta RICAP_BETA RICAP beta (default: 0.3) --ricap-with-line RICAP with boundary line (default: False) --icap apply ICAP (default: False) --icap-beta ICAP_BETA ICAP beta (default: 0.3) --cutmix apply CutMix (default: False) --cutmix-beta CUTMIX_BETA CutMix beta (default: 1.0) --cutmix-prob CUTMIX_PROB CutMix probability (default: 1.0)