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github.com/Hawkeye-FineGrained/Hawkeye
/ types & classes
Types & classes
105 in github.com/Hawkeye-FineGrained/Hawkeye
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Functions
503
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Types & classes
105
↓ 12 callers
Class
Classifier
model/methods/MGE_CNN/MGE.py:18
↓ 7 callers
Class
PerformanceMeter
Record the performance metric during training
utils/utils.py:10
↓ 6 callers
Class
Bottleneck1x1
model/methods/Interp_Parts.py:212
↓ 5 callers
Class
ResNet
model/methods/NTS_Net/resnet.py:94
↓ 4 callers
Class
Flatten
model/methods/APCNN.py:298
↓ 3 callers
Class
AverageMeter
Keep track of most recent, average, sum, and count of a metric.
utils/utils.py:32
↓ 3 callers
Class
BalancedBatchSampler
dataset/sampler.py:5
↓ 3 callers
Class
ChannelGate
generation channel attention mask
model/methods/APCNN.py:283
↓ 3 callers
Class
RegularLoss
model/loss/CrossX_loss.py:6
↓ 3 callers
Class
SpatialGate
generation spatial attention mask
model/methods/APCNN.py:271
↓ 2 callers
Class
BasicConv
model/methods/APCNN.py:73
↓ 2 callers
Class
FGDataset
dataset/dataset.py:22
↓ 2 callers
Class
GradCam
model/methods/MGE_CNN/grad_cam.py:51
↓ 2 callers
Class
Repository
A dict format repository to register module. Repository can also manage config node.
utils/repository.py:1
↓ 2 callers
Class
ResNet
model/methods/Interp_Parts.py:251
↓ 2 callers
Class
ResNet
implementation of AP-CNN on ResNet
model/methods/APCNN.py:344
↓ 2 callers
Class
ScaleLayer
model/methods/S3N.py:105
↓ 2 callers
Class
TqdmHandler
utils/utils.py:69
↓ 1 callers
Class
APCNNTrainer
Examples/APCNN.py:14
↓ 1 callers
Class
APINetLoss
model/loss/APINet_loss.py:5
↓ 1 callers
Class
APINetTrainer
Examples/APINet.py:13
↓ 1 callers
Class
BCNNTrainer
Examples/BCNN.py:10
↓ 1 callers
Class
BaselineTrainer
Examples/Baseline.py:8
↓ 1 callers
Class
BaselineTrainer
Examples/DCL.py:14
↓ 1 callers
Class
BilinearPooling
model/methods/BCNN.py:8
↓ 1 callers
Class
Branch
model/methods/ProtoTree/branch.py:7
↓ 1 callers
Class
CBCNNTrainer
Examples/CBCNN.py:9
↓ 1 callers
Class
CINClassifier
Channel Interaction Network Classifier
model/methods/CIN.py:63
↓ 1 callers
Class
CINLoss
model/loss/CIN_loss.py:7
↓ 1 callers
Class
CINTrainer
Examples/CIN.py:13
↓ 1 callers
Class
ChannelInteractionModule
Channel Interaction Network
model/methods/CIN.py:10
↓ 1 callers
Class
ClassificationPresetEval
dataset/transforms.py:52
↓ 1 callers
Class
ClassificationPresetTrain
dataset/transforms.py:14
↓ 1 callers
Class
CompactBilinearPooling
Compute compact bilinear pooling over two bottom inputs. Args: output_dim: output dimension for compact bilinear pooling.
model/methods/CBCNN.py:38
↓ 1 callers
Class
CrossXLoss
model/loss/CrossX_loss.py:31
↓ 1 callers
Class
CrossXTrainer
Examples/CrossX.py:12
↓ 1 callers
Class
DCLDataset
dataset/dataset_DCL.py:11
↓ 1 callers
Class
DCLLoss
model/loss/DCL_loss.py:4
↓ 1 callers
Class
FeatureExtractor
Class for extracting activations and registering gradients from targetted intermediate layers
model/methods/MGE_CNN/grad_cam.py:5
↓ 1 callers
Class
GroupingUnit
model/methods/Interp_Parts.py:25
↓ 1 callers
Class
InterpPartsLoss
model/loss/InterpParts_loss.py:12
↓ 1 callers
Class
InterpPartsNetTrainer
Examples/InterpPartsNet.py:12
↓ 1 callers
Class
KernelGenerator
model/methods/S3N.py:25
↓ 1 callers
Class
L2Conv2D
Convolutional layer that computes the squared L2 distance instead of the conventional inner product.
model/methods/ProtoTree/l2conv.py:6
↓ 1 callers
Class
Leaf
model/methods/ProtoTree/leaf.py:8
↓ 1 callers
Class
LocalCamNet
model/methods/MGE_CNN/MGE.py:75
↓ 1 callers
Class
MAMCLoss
model/loss/MAMC_loss.py:6
↓ 1 callers
Class
MELayer
model/methods/CrossX.py:47
↓ 1 callers
Class
MGE_CNNTrainer
Examples/MGE_CNN.py:11
↓ 1 callers
Class
MPNCOV
Matrix power normalized Covariance pooling (MPNCOV) implementation of fast MPN-COV (i.e.,iSQRT-COV) https://arxiv.org/abs/1712.01034
model/methods/MPNCOV.py:41
↓ 1 callers
Class
MPNTrainer
Examples/MPN.py:9
↓ 1 callers
Class
ModelOutputs
Class for making a forward pass, and getting: 1. The network output. 2. Activations from intermeddiate targetted layers. 3. Gradients fro
model/methods/MGE_CNN/grad_cam.py:30
↓ 1 callers
Class
MultiSmoothLoss
Multi smooth loss.
model/loss/S3N_loss.py:6
↓ 1 callers
Class
MyTrainer
Examples/ProtoTreeNet.py:12
↓ 1 callers
Class
NPairsLoss
N-pairs loss as explained in equation 11 of MAMC paper. Reference: Multi-Attention Multi-Class Constraint for Fine-grained Image Recognit
model/loss/MAMC_loss.py:24
↓ 1 callers
Class
NTSLoss
model/loss/NTS_loss.py:6
↓ 1 callers
Class
NTSTrainer
Examples/NTSNet.py:11
↓ 1 callers
Class
OSME
model/methods/OSME.py:27
↓ 1 callers
Class
OSMENetTrainer
Examples/OSMENet.py:13
↓ 1 callers
Class
OSME_block
model/methods/OSME.py:8
↓ 1 callers
Class
PCResNetTrainer
Examples/PairConfusion.py:10
↓ 1 callers
Class
PLTrainer
Examples/PeerLearning.py:16
↓ 1 callers
Class
PairwiseConfusionLoss
model/loss/pair_confusion.py:8
↓ 1 callers
Class
ProposalNet
model/methods/NTS_Net/NTSNet.py:63
↓ 1 callers
Class
ProtoTree
model/methods/ProtoTree/prototree.py:18
↓ 1 callers
Class
PyramidAttentions
Attention pyramid module with bottom-up attention pathway
model/methods/APCNN.py:236
↓ 1 callers
Class
PyramidFeatures
Feature pyramid module with top-down feature pathway
model/methods/APCNN.py:202
↓ 1 callers
Class
RandomCutmix
Randomly apply Cutmix to the provided batch and targets. The class implements the data augmentations as described in the paper `"CutMix: Regul
dataset/transforms.py:152
↓ 1 callers
Class
RandomMixup
Randomly apply Mixup to the provided batch and targets. The class implements the data augmentations as described in the paper `"mixup: Beyond
dataset/transforms.py:76
↓ 1 callers
Class
RandomSwap
dataset/transforms.py:243
↓ 1 callers
Class
ResNet
model/backbone/resnet.py:147
↓ 1 callers
Class
ResNet
model/methods/CrossX.py:126
↓ 1 callers
Class
S3NTrainer
Examples/S3N.py:12
↓ 1 callers
Class
SimpleFPA
model/methods/APCNN.py:170
↓ 1 callers
Class
Tester
Test a model from a config which could be a training config.
test.py:14
↓ 1 callers
Class
Timer
utils/utils.py:79
↓ 1 callers
Class
Trainer
Base trainer
train.py:37
↓ 1 callers
Class
VGG
model/backbone/vgg.py:25
Class
APINet
model/methods/APINet.py:10
Class
BCNN
model/methods/BCNN.py:31
Class
BasicBlock
model/backbone/resnet.py:40
Class
BasicBlock
model/methods/Interp_Parts.py:179
Class
BasicBlock
model/methods/APCNN.py:99
Class
BasicBlock
model/methods/NTS_Net/resnet.py:23
Class
Bottleneck
model/backbone/resnet.py:89
Class
Bottleneck
model/methods/Interp_Parts.py:139
Class
Bottleneck
model/methods/APCNN.py:131
Class
Bottleneck
model/methods/CrossX.py:73
Class
Bottleneck
model/methods/NTS_Net/resnet.py:55
Class
CBCNN
model/methods/CBCNN.py:13
Class
CIN
model/methods/CIN.py:85
Class
Covpool
model/methods/MPNCOV.py:105
Class
DCL
model/methods/DCL.py:9
Class
MPN
model/methods/MPNCOV.py:24
Class
MixupCutmixCollateFn
dataset/collate_fn.py:8
Class
NTSNet
model/methods/NTS_Net/NTSNet.py:11
Class
Node
model/methods/ProtoTree/node.py:4
Class
OSMENet
model/methods/OSME.py:48
Class
PeakStimulation
model/methods/S3N.py:57
Class
PeerLearningNet
model/methods/PeerLearningNet.py:9
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