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app.py

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+import gradio as gr
+import torch
+import numpy as np
+
+import torch
+import torchvision.models as models
+from torch import nn
+
+from model import *
+
+from albumentations import (
+    HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
+    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
+    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
+    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout, CoarseDropout, ShiftScaleRotate, CenterCrop, Resize, Rotate,
+    ShiftScaleRotate, CenterCrop, Crop, Resize, Rotate, RandomShadow, RandomSizedBBoxSafeCrop,
+    ChannelShuffle, MotionBlur,Lambda,SmallestMaxSize
+)
+
+from albumentations.pytorch import ToTensorV2
+
+# Your Albumentations transformations
+test_transforms = Compose([
+    Resize(224, 224),
+    Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
+    ToTensorV2(),
+])
+
+def load_cub200_classes():
+    """
+    This function loads the classes from the classes.txt file and returns a dictionary
+    """
+    with open("classes.txt", encoding="utf-8") as f:
+        classes = f.read().splitlines()
+
+    # convert classes to dictionary separating the lines by the first space
+    classes = {int(line.split(" ")[0]) : line.split(" ")[1] for line in classes}
+
+    # return the classes dictionary
+    return classes
+
+def load_model():
+    """
+    This function loads the trained model and returns it
+    """
+    model = models.resnet50(pretrained=True)
+
+    # Freeze the initial layers up to a certain point (e.g., layer 4)
+    freeze_layers = 4
+    for idx, (name, param) in enumerate(model.named_parameters()):
+        if idx < freeze_layers:
+            param.requires_grad = False
+
+    # Replace the final fully connected layer for the new task
+    num_ftrs = model.fc.in_features
+    model.fc = nn.Linear(num_ftrs, 200)
+
+    # Add dropout layers
+    model = nn.Sequential(
+        model,
+        nn.Dropout(0.8),  # Adjust the dropout rate as needed
+        nn.Linear(200, 200)  # Add additional fully connected layer
+    )
+
+    # Load the state dictionary from the file
+    state_dict = torch.load("resnet50_7511.pt",map_location=torch.device('cpu'))
+
+    # Load the state dictionary into the model object
+    model.load_state_dict(state_dict)
+
+    # # Load actual model 
+    # model = torch.load("resnet18.pth", map_location=torch.device('cpu'))
+
+    # set the model to evaluation mode
+    model.eval()
+
+    # return the model
+    return model
+
+def predict_image(image):
+    """
+    This function takes an image as input and returns the class label
+    """
+    # load the model
+    model = load_model()
+    # model.eval()
+    # load the classes
+    classes = load_cub200_classes()
+
+     # Apply Albumentations transformations
+    transformed_image = test_transforms(image=image)['image']
+
+    # Convert the image to a PyTorch tensor
+    tensor_image = transformed_image.unsqueeze(0)
+
+    # Make prediction
+    with torch.no_grad():
+        output = model(tensor_image)
+
+    # Assuming the output is a tensor representing class probabilities
+    probabilities = torch.nn.functional.softmax(output[0], dim=0).numpy()
+
+    # Get the class with the highest probability
+    predicted_class = np.argmax(probabilities)
+
+    # Return the class label
+    return "Predicted Class: " + classes[predicted_class+1]
+
+# create a gradio interface
+gr.Interface(fn=predict_image, inputs="image", outputs="text").launch()

classes.txt

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+1 001.Black_footed_Albatross
+2 002.Laysan_Albatross
+3 003.Sooty_Albatross
+4 004.Groove_billed_Ani
+5 005.Crested_Auklet
+6 006.Least_Auklet
+7 007.Parakeet_Auklet
+8 008.Rhinoceros_Auklet
+9 009.Brewer_Blackbird
+10 010.Red_winged_Blackbird
+11 011.Rusty_Blackbird
+12 012.Yellow_headed_Blackbird
+13 013.Bobolink
+14 014.Indigo_Bunting
+15 015.Lazuli_Bunting
+16 016.Painted_Bunting
+17 017.Cardinal
+18 018.Spotted_Catbird
+19 019.Gray_Catbird
+20 020.Yellow_breasted_Chat
+21 021.Eastern_Towhee
+22 022.Chuck_will_Widow
+23 023.Brandt_Cormorant
+24 024.Red_faced_Cormorant
+25 025.Pelagic_Cormorant
+26 026.Bronzed_Cowbird
+27 027.Shiny_Cowbird
+28 028.Brown_Creeper
+29 029.American_Crow
+30 030.Fish_Crow
+31 031.Black_billed_Cuckoo
+32 032.Mangrove_Cuckoo
+33 033.Yellow_billed_Cuckoo
+34 034.Gray_crowned_Rosy_Finch
+35 035.Purple_Finch
+36 036.Northern_Flicker
+37 037.Acadian_Flycatcher
+38 038.Great_Crested_Flycatcher
+39 039.Least_Flycatcher
+40 040.Olive_sided_Flycatcher
+41 041.Scissor_tailed_Flycatcher
+42 042.Vermilion_Flycatcher
+43 043.Yellow_bellied_Flycatcher
+44 044.Frigatebird
+45 045.Northern_Fulmar
+46 046.Gadwall
+47 047.American_Goldfinch
+48 048.European_Goldfinch
+49 049.Boat_tailed_Grackle
+50 050.Eared_Grebe
+51 051.Horned_Grebe
+52 052.Pied_billed_Grebe
+53 053.Western_Grebe
+54 054.Blue_Grosbeak
+55 055.Evening_Grosbeak
+56 056.Pine_Grosbeak
+57 057.Rose_breasted_Grosbeak
+58 058.Pigeon_Guillemot
+59 059.California_Gull
+60 060.Glaucous_winged_Gull
+61 061.Heermann_Gull
+62 062.Herring_Gull
+63 063.Ivory_Gull
+64 064.Ring_billed_Gull
+65 065.Slaty_backed_Gull
+66 066.Western_Gull
+67 067.Anna_Hummingbird
+68 068.Ruby_throated_Hummingbird
+69 069.Rufous_Hummingbird
+70 070.Green_Violetear
+71 071.Long_tailed_Jaeger
+72 072.Pomarine_Jaeger
+73 073.Blue_Jay
+74 074.Florida_Jay
+75 075.Green_Jay
+76 076.Dark_eyed_Junco
+77 077.Tropical_Kingbird
+78 078.Gray_Kingbird
+79 079.Belted_Kingfisher
+80 080.Green_Kingfisher
+81 081.Pied_Kingfisher
+82 082.Ringed_Kingfisher
+83 083.White_breasted_Kingfisher
+84 084.Red_legged_Kittiwake
+85 085.Horned_Lark
+86 086.Pacific_Loon
+87 087.Mallard
+88 088.Western_Meadowlark
+89 089.Hooded_Merganser
+90 090.Red_breasted_Merganser
+91 091.Mockingbird
+92 092.Nighthawk
+93 093.Clark_Nutcracker
+94 094.White_breasted_Nuthatch
+95 095.Baltimore_Oriole
+96 096.Hooded_Oriole
+97 097.Orchard_Oriole
+98 098.Scott_Oriole
+99 099.Ovenbird
+100 100.Brown_Pelican
+101 101.White_Pelican
+102 102.Western_Wood_Pewee
+103 103.Sayornis
+104 104.American_Pipit
+105 105.Whip_poor_Will
+106 106.Horned_Puffin
+107 107.Common_Raven
+108 108.White_necked_Raven
+109 109.American_Redstart
+110 110.Geococcyx
+111 111.Loggerhead_Shrike
+112 112.Great_Grey_Shrike
+113 113.Baird_Sparrow
+114 114.Black_throated_Sparrow
+115 115.Brewer_Sparrow
+116 116.Chipping_Sparrow
+117 117.Clay_colored_Sparrow
+118 118.House_Sparrow
+119 119.Field_Sparrow
+120 120.Fox_Sparrow
+121 121.Grasshopper_Sparrow
+122 122.Harris_Sparrow
+123 123.Henslow_Sparrow
+124 124.Le_Conte_Sparrow
+125 125.Lincoln_Sparrow
+126 126.Nelson_Sharp_tailed_Sparrow
+127 127.Savannah_Sparrow
+128 128.Seaside_Sparrow
+129 129.Song_Sparrow
+130 130.Tree_Sparrow
+131 131.Vesper_Sparrow
+132 132.White_crowned_Sparrow
+133 133.White_throated_Sparrow
+134 134.Cape_Glossy_Starling
+135 135.Bank_Swallow
+136 136.Barn_Swallow
+137 137.Cliff_Swallow
+138 138.Tree_Swallow
+139 139.Scarlet_Tanager
+140 140.Summer_Tanager
+141 141.Artic_Tern
+142 142.Black_Tern
+143 143.Caspian_Tern
+144 144.Common_Tern
+145 145.Elegant_Tern
+146 146.Forsters_Tern
+147 147.Least_Tern
+148 148.Green_tailed_Towhee
+149 149.Brown_Thrasher
+150 150.Sage_Thrasher
+151 151.Black_capped_Vireo
+152 152.Blue_headed_Vireo
+153 153.Philadelphia_Vireo
+154 154.Red_eyed_Vireo
+155 155.Warbling_Vireo
+156 156.White_eyed_Vireo
+157 157.Yellow_throated_Vireo
+158 158.Bay_breasted_Warbler
+159 159.Black_and_white_Warbler
+160 160.Black_throated_Blue_Warbler
+161 161.Blue_winged_Warbler
+162 162.Canada_Warbler
+163 163.Cape_May_Warbler
+164 164.Cerulean_Warbler
+165 165.Chestnut_sided_Warbler
+166 166.Golden_winged_Warbler
+167 167.Hooded_Warbler
+168 168.Kentucky_Warbler
+169 169.Magnolia_Warbler
+170 170.Mourning_Warbler
+171 171.Myrtle_Warbler
+172 172.Nashville_Warbler
+173 173.Orange_crowned_Warbler
+174 174.Palm_Warbler
+175 175.Pine_Warbler
+176 176.Prairie_Warbler
+177 177.Prothonotary_Warbler
+178 178.Swainson_Warbler
+179 179.Tennessee_Warbler
+180 180.Wilson_Warbler
+181 181.Worm_eating_Warbler
+182 182.Yellow_Warbler
+183 183.Northern_Waterthrush
+184 184.Louisiana_Waterthrush
+185 185.Bohemian_Waxwing
+186 186.Cedar_Waxwing
+187 187.American_Three_toed_Woodpecker
+188 188.Pileated_Woodpecker
+189 189.Red_bellied_Woodpecker
+190 190.Red_cockaded_Woodpecker
+191 191.Red_headed_Woodpecker
+192 192.Downy_Woodpecker
+193 193.Bewick_Wren
+194 194.Cactus_Wren
+195 195.Carolina_Wren
+196 196.House_Wren
+197 197.Marsh_Wren
+198 198.Rock_Wren
+199 199.Winter_Wren
+200 200.Common_Yellowthroat

cub_classification_resnet.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:621c61bf81ea6e924af6ce58411b4bb470823449c979bb3a17eedf9cc63caf62
+size 95999162

requirements.txt

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+gradio
+torch
+numpy
+albumentations

resnet.pt

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+size 102540270

resnet101.pth

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+size 172320698

resnet18.pth

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+oid sha256:6ae575d4c22919fd951dc28d865993d0e6366d9a7f5b9ca0250ed45d79e52ac2
+size 45371066

resnet50_7511.pt

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+oid sha256:2ce0d050b523cc67485a6bc94e7fe57c74ffec4622d51a500c7f0554c56988d2
+size 96152908

resnet_classification.pt

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