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Parent(s):
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Initial commit
Browse files- .gitattributes +1 -0
- app.py +73 -0
- class_names.txt +101 -0
- effnetb2_food101_model.pth +3 -0
- example/04-pizza-dad.jpg +3 -0
- model.py +24 -0
- requirements.txt +4 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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example/04-pizza-dad.jpg filter=lfs diff=lfs merge=lfs -text
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app.py
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# Import and class names setup
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import gradio as gr
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import os
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import torch
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from model import create_effnetb2_model
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from timeit import default_timer as timer
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from typings import Tuple, Dict
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import class_names
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# Setup class names
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with open(class_names.txt, 'r') as f:
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class_names= [food_name.strip() for food_name in f.readlines()]
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# Model and transforms preparation
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effnetb2_model, effnetb2_transform= create_effnetb2_model()
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# Load state dict
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effnetb2_model.load_state_dict(torch.load(
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f= 'effnetb2_food101_model.pth',
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map_location= torch.device('cpu')
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)
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)
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# Predict function
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def predict(img)-> Tuple[Dict, float]:
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# start a timer
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start_time= timer()
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#transform the input image for use with effnet b2
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transform_image= effnetb2_transform(img).unsqueeze(0)
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#put model into eval mode, make pred
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effnetb2_model.eval()
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with torch.inference_mode():
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pred_logits= effnetb2_model(transform_image)
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pred_prob= torch.softmax(pred_logits, dim=1)
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# create a pred label and pred prob dict
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pred_label_and_prob= {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))}
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# calc pred time
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stop_time= timer()
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pred_time= round(stop_time - start_time, 4)
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# return pred dict and pred time
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return pred_label_and_prob, pred_time
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# create example list
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example_list= [['example/'+example] for example in os.listdir('example')]
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# create gradio app
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title= 'FoodVision Large 🍕🥩🍣 '
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description= 'An EfficientnetB2 feature extractor Computer vision model to classify 101 classes of food from the food 101 image dataset'
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article= 'Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/).'
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# Create the gradio demo
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demo= gr.Interface(fn= predict,
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inputs=gr.Image(type='pil'),
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outputs= [gr.Label(num_top_classes=5, label= 'predictions'),
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gr.Number(label= 'Prediction time (S)')],
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examples= example_list,
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title= title,
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description= description,
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article= article
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)
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# Launch the demo
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demo.launch()
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class_names.txt
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apple_pie
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baby_back_ribs
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baklava
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beef_carpaccio
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beef_tartare
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beet_salad
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beignets
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bibimbap
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bread_pudding
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breakfast_burrito
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bruschetta
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caesar_salad
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cannoli
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caprese_salad
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carrot_cake
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ceviche
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cheese_plate
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cheesecake
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chicken_curry
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chicken_quesadilla
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chicken_wings
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chocolate_cake
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chocolate_mousse
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churros
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clam_chowder
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club_sandwich
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crab_cakes
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creme_brulee
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croque_madame
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cup_cakes
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deviled_eggs
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donuts
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dumplings
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edamame
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eggs_benedict
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escargots
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falafel
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filet_mignon
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fish_and_chips
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foie_gras
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french_fries
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french_onion_soup
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french_toast
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fried_calamari
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fried_rice
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frozen_yogurt
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garlic_bread
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gnocchi
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greek_salad
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grilled_cheese_sandwich
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grilled_salmon
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guacamole
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gyoza
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hamburger
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hot_and_sour_soup
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hot_dog
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huevos_rancheros
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hummus
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ice_cream
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lasagna
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lobster_bisque
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lobster_roll_sandwich
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macaroni_and_cheese
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macarons
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miso_soup
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mussels
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nachos
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omelette
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onion_rings
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oysters
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pad_thai
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paella
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pancakes
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panna_cotta
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peking_duck
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pho
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pizza
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pork_chop
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poutine
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prime_rib
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pulled_pork_sandwich
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ramen
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ravioli
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red_velvet_cake
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risotto
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samosa
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sashimi
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scallops
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seaweed_salad
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shrimp_and_grits
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spaghetti_bolognese
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spaghetti_carbonara
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spring_rolls
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steak
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strawberry_shortcake
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sushi
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tacos
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takoyaki
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tiramisu
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tuna_tartare
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waffles
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effnetb2_food101_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:6bc414d33be77a0e4a1e0c50bd6c69f683ea53bdaac5e4ae3abad09e28499ce7
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size 31833771
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example/04-pizza-dad.jpg
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![]() |
Git LFS Details
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model.py
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import torch
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import torchvision
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from torch import nn
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def create_effnetb2_model(num_classes:int=101,
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seed:int=42):
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# Create Effnet pretrained model
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weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
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transforms= weights.transforms()
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model= torchvision.models.efficientnet_b2(weights=weights)
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# Freeze all layers in the base model
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for param in model.parameters():
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param.requires_grad= False
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# Change the classifier layer
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torch.manual_seed(seed)
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model.classifier= nn.Sequential(
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nn.Dropout(p=0.3, inplace= True),
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nn.Linear(in_features=1408, out_features= num_classes)
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)
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return model, transforms
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requirements.txt
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torch==2.0.1
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torchvision==0.15.2
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gradio==3.35.2
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