This is an experimental setup to build code base for pytorch. Its main is to experiment faster using transfer learning on all available pre-trained models.

Dataset: Plant Seedlings classification

Classes present:

• Black-grass
• Charlock
• Cleavers
• Common Chickweed
• Common wheat
• Fat Hen
• Loose Silky-bent
• Maize
• Scentless Mayweed
• Shepherds Purse
• Small-flowered Cranesbill
• Sugar beet

Run the following commands

To train the models:

make -f Makefile.train

To test the models:

make -f Makefile.predict

Results

total number of train images: 4268 total number of val images: 482 total number of test images: 794

Results with Full-Agumentation strategy:

Trained the networks in three methods:

• Full Finetuning
• Freeze first few layers

Case-1 - Finetuning entire network

Models	Train Accuracy_score	Val Accuracy_score
resnet18	0.92783	0.93153
resnet34	0.9522	0.94190
resnet50	0.95665	0.94398
resnet101	0.96696	0.96265
resnet152	0.96555	0.95643
squeezenet1_0	0.94329	0.92738
squeezenet1_1	0.93955	0.93153
densenet121	0.95243	0.9336
densenet169	0.96626	0.93983
densenet201	0.96063	0.95020
inception_v3	0.94212	0.93568
vgg11	0.93814	0.93153
vgg13	0.94493	0.94190
vgg16	0.95665	0.93568
vgg19	0.95009	0.93775
vgg11_bn	0.94142	0.93775
vgg13_bn	0.94423	0.92738
vgg16_bn	0.94634	0.94190
vgg19_bn	0.94915	0.93360
alexnet	0.91260	0.90456
resnext101_64x4d	0.98055	0.96887
resnext101_32x4d	0.98172	0.96887
nasnetalarge	0.96907	0.96265
inceptionresnetv2	0.96134	0.95435
inceptionv4	0.96930	0.96473

Case-2 - Freezed first few layers(look at code)

Models	Train Accuracy_score	Val Accuracy_score
resnet18	0.9196	0.91493
resnet34	0.94845	0.93983
resnet50	0.9564	0.93983
resnet101	0.96790	0.96265
resnet152	0.96508	0.95643
squeezenet1_0	0.94048	0.92738
squeezenet1_1	0.93088	0.92116
densenet121	0.95173	0.95228
densenet169	0.96087	0.94813
densenet201	0.95384	0.95020
inception_v3	0.94025	0.93775
vgg11	0.93697	0.92946
vgg13	0.93533	0.92323
vgg16	0.94821	0.93983
vgg19	0.95243	0.94190
vgg11_bn	0.93416	0.92738
vgg13_bn	0.93322	0.92323
vgg16_bn	0.94728	0.93775
vgg19_bn	0.94798	0.94190
alexnet	0.89784	0.88589
resnext101_64x4d	0.98617	0.96887
resnext101_32x4d	0.98195	0.96473
nasnetalarge	0.95970	0.96265
inceptionresnetv2	0.95103	0.94813
inceptionv4	0.96251	0.93775

Results with Basic Agumentation

Case-1 (Freezed all layers except last one)

Models	Train Accuracy_score	Val Accuracy_score
resnet18	0.77553	0.75518
resnet152	0.82778	0.81535
resnet101	0.82333	0.80290
resnet50	0.79943	0.78630
resnet34	0.78655	0.74688
squeezenet1_0	0.91447	0.87966
squeezenet1_1	0.90089	0.87344
densenet121	0.80880	0.81120
densenet169	0.84746	0.82987
densenet201	0.86621	0.86514
inception_v3	0.76101	0.74688
vgg11	0.78209	0.78008
vgg13	0.75960	0.72821
vgg16	0.77038	0.71576
vgg19	0.71204	0.64522
vgg11_bn	0.76522	0.74481
vgg13_bn	0.76241	0.76348
vgg16_bn	0.76265	0.75726
vgg19_bn	0.75773	0.73858
alexnet	0.83153	0.76348

Case-2 Finetuning the entire network

Models	Train Accuracy_score	Val Accuracy_score
resnet18	0.98477	0.96058
resnet152	0.99273	0.97717
resnet101	0.99367	0.97717
resnet50	0.99133	0.97510
resnet34	0.98969	0.97095
squeezenet1_0	0.96274	0.94190
squeezenet1_1	0.96485	0.92738
densenet121	0.99086	0.96887
densenet169	0.99507	0.97510
densenet201	0.99390	0.97717
inception_v3	0.98898	0.97302
vgg11	0.98031	0.95020
vgg13	0.98078	0.95643
vgg16	0.98266	0.95435
vgg19	0.98430	0.95643
vgg11_bn	0.98570	0.96265
vgg13_bn	0.98687	0.97095
vgg16_bn	0.99179	0.96680
vgg19_bn	0.99297	0.96680
alexnet	0.95970	0.92946

Submissions:

• densenet201 LB - to 97.22
• ensemble1 - mode of all best performing models LB - 97.32

TO_DO

1. Adding mixup strategy to all the networks
2. Ensembling model outputs
3. Model stacking
4. Extracting bottleneck features and using - ML to train the model - Visualization using T-sne
5. Solve issue with bninception(Model is not training)
6. Train Vggm network