First attempt

.devcontainer/devcontainer.json

@@ -0,0 +1,46 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/python
+{
+	"name": "Python 3",
+	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+	"build": {
+		"context": "..",
+		// Path is relative to the devcontainer.json file.
+		"dockerfile": "../Dockerfile"
+	},
+	"runArgs": [
+		// "--gpus",
+		// "all",
+		"--network=host",
+		"--ipc=host",
+		"--ulimit",
+		"memlock=-1",
+		"--ulimit",
+		"stack=67108864"
+	],
+	// "mounts": [ // Add this if you need to mount your project directory
+	// 	"source=/mnt,target=/mnt,type=bind",
+	// 	"source=${localEnv:HOME}/datasets/,target=/root/datasets,type=bind",
+	// 	"source=${localWorkspaceFolder},target=/app,type=bind"
+	// ],
+	"customizations": {
+		"vscode": {
+			"extensions": [
+				"shiro.pythonpack",
+				"charliermarsh.ruff"
+			]
+		}
+	}
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [
+	// 	8080
+	// ]
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+	// Use 'postCreateCommand' to run commands after the container is created.
+	// "postCreateCommand": "pip3 install --user -r requirements.txt",
+	// Configure tool-specific properties.
+	// "customizations": {},
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

Dockerfile

@@ -1,14 +1,16 @@
 FROM python:3.11
 
+ENV PIP_PROGRESS_BAR=on
+
 WORKDIR /code
 
 COPY ./requirements.txt /code/requirements.txt
 RUN python3 -m pip install --no-cache-dir --upgrade pip
-RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+# RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 COPY . .
 
-CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
+# CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
 
 RUN mkdir /.cache
 RUN chmod 777 /.cache

app.py

@@ -1,147 +1,314 @@
 import io
 import random
+from io import BytesIO
 from typing import List, Tuple
 
 import aiohttp
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+import torch
+from bokeh.themes import Theme
 
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
+# import torchvision.transforms.functional as TVF
+import torch.nn.functional as F
+from PIL import Image
+from transformers import AutoImageProcessor, ResNetForImageClassification
+from transformers.image_transforms import to_pil_image
 
+DEVICE = "cpu"
 
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
+pn.extension("mathjax", design="bootstrap", sizing_mode="stretch_width")
 
 
 @pn.cache
 def load_processor_model(
     processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
+) -> Tuple[AutoImageProcessor, ResNetForImageClassification]:
+    processor = AutoImageProcessor.from_pretrained(processor_name)
+    model = ResNetForImageClassification.from_pretrained(model_name)
     return processor, model
 
 
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
+def denormalize(image, mean, std):
+    mean = torch.tensor(mean).view(1, -1, 1, 1)  # Reshape for broadcasting
+    std = torch.tensor(std).view(1, -1, 1, 1)
+    return image * std + mean
+
 
+# FGSM attack code
+def fgsm_attack(image, epsilon, data_grad):
+    # Collect the element-wise sign of the data gradient
+    sign_data_grad = data_grad.sign()
+    # Create the perturbed image by adjusting each pixel of the input image
+    perturbed_image = image + epsilon * sign_data_grad
+    # Adding clipping to maintain [0,1] range
+    perturbed_image = torch.clamp(perturbed_image, 0, 1)
+    # Return the perturbed image
+    return perturbed_image.detach()
 
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
+
+def run_forward_backward(image: Image, epsilon):
     processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
+        "microsoft/resnet-18", "microsoft/resnet-18"
+    )
+
+    # Grab input
+    input_tensor = processor(image, return_tensors="pt")["pixel_values"]
+    input_tensor.requires_grad_(True)
+
+    # Run inference
+    output = model(input_tensor)
+    output = output.logits
+
+    # Top target
+    top_pred = output.max(1, keepdim=False)[1]
+
+    # Get NLL loss and backward
+    loss = F.cross_entropy(output, top_pred)
+    model.zero_grad()
+    loss.backward()
+
+    # Denormalize input
+    mean = torch.tensor(processor.image_mean).view(1, -1, 1, 1)
+    std = torch.tensor(processor.image_std).view(1, -1, 1, 1)
+    input_tensor_denorm = input_tensor.detach() * std + mean
+
+    # FGSM attack
+    adv_input_tensor_denorm = fgsm_attack(
+        image=input_tensor_denorm, epsilon=epsilon, data_grad=input_tensor.grad.data
     )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
+
+    # Normalize adversarial input tensor back to the input range
+    adv_input_tensor = (adv_input_tensor_denorm - mean) / std
+
+    # Inference on adversarial image
+    adv_output = model(adv_input_tensor)
+    adv_output = adv_output.logits
+
+
+    return (
+        output,
+        adv_output,
+        input_tensor_denorm.squeeze(),
+        adv_input_tensor_denorm.squeeze(),
     )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
 
 
-async def process_inputs(class_names: List[str], image_url: str):
+async def process_inputs(button_event, image_data: bytes, epsilon: float):
     """
     High level function that takes in the user inputs and returns the
     classification results as panel objects.
     """
     try:
         main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
+        # if not button_event or (button_event and not isinstance(image_data, bytes)):
+        if not isinstance(image_data, bytes):
+            yield "##### 👋 Upload an image to proceed"
             return
-    
+
         yield "##### ⚙ Fetching image and running model..."
         try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
+            # Open the image using PIL
+            pil_img = Image.open(BytesIO(image_data))
+                
+            # Run forward + FGSM
+            clean_logits, adv_logits, input_tensor, adv_input_tensor = run_forward_backward(
+                image=pil_img, epsilon=epsilon
+            )
+
         except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
+            yield f"##### Something went wrong, please try a different image! \n {e}"
             return
+
+        img = pn.pane.Image(
+            to_pil_image(input_tensor, do_rescale=True),
+            height=350,
+            align="center",
+        )
+
+        # Convert image for visualizing
+        adv_img = pn.pane.Image(
+            to_pil_image(adv_input_tensor, do_rescale=True),
+            height=350,
+            align="center",
+        )
+
+        # Build the results column
+        k_val = 5
+        results = pn.Column(
+            pn.Row("###### Uploaded", "###### Adversarial"), pn.Row(img, adv_img), f" ###### Top {k_val} class predictions",
+        )
+
+        # Get likelihoods
+        likelihoods = [
+            F.softmax(clean_logits, dim=1).squeeze(),
+            F.softmax(adv_logits, dim=1).squeeze(),
+        ]
+
+        label_bars_rows = pn.Row()
+
+        for likelihood_tensor in likelihoods:
+            # Get top k values and indices
+            vals_topk_clean, idx_topk_clean = torch.topk(likelihood_tensor, k=k_val)
+            label_bars = pn.Column()
+            
+            for idx, val in zip(idx_topk_clean, vals_topk_clean):
+                prob = val.item()
+                row_label = pn.widgets.StaticText(
+                    name=f"{classes[idx]}", 
+                    value=f"{prob:.2%}", 
+                    align="center"
+                )
+                row_bar = pn.indicators.Progress(
+                    value=int(prob * 100),
+                    sizing_mode="stretch_width",
+                    bar_color="success" if prob > 0.7 else "warning",  # Dynamic color based on value
+                    margin=(0, 10),
+                    design=pn.theme.Material,
+                )
+                label_bars.append(pn.Column(row_label, row_bar))
+
+        # for likelihood_tensor in likelihoods:
+        #     # Get top
+        #     vals_topk_clean, idx_topk_clean = torch.topk(likelihood_tensor, k=k_val)
+        #     label_bars = pn.Column()
+        #     for idx, val in zip(idx_topk_clean, vals_topk_clean):
+        #         prob = val.item()
+        #         row_label = pn.widgets.StaticText(
+        #             name=f"{classes[idx]}", value=f"{prob:.2%}", align="center"
+        #         )
+        #         row_bar = pn.indicators.Progress(
+        #             value=int(prob * 100),
+        #             sizing_mode="stretch_width",
+        #             bar_color="secondary",
+        #             margin=(0, 10),
+        #             design=pn.theme.Material,
+        #         )
+        #         label_bars.append(pn.Column(row_label, row_bar))
+
+            label_bars_rows.append(label_bars)
     
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
+        results.append(label_bars_rows)
+
         yield results
+
+    except Exception as e:
+        yield f"##### Something went wrong! \n {e}"
+        return
+    
     finally:
         main.disabled = False
 
 
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
+####################################################################################################################################
+# Get classes
+classes = []
+with open("classes.txt", "r") as file:
+    classes = file.read()
+    classes = classes.split("\n")
 
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
+# Create widgets
+############################################
+# Fil upload widget
+file_input = pn.widgets.FileInput(name="Upload a PNG image", accept=".png,.jpg")
+
+# Epsilon
+epsilon_slider = pn.widgets.FloatSlider(
+    name=r"$$\epsilon$$", start=0, end=0.1, step=0.005, value=0.05, format='1[.]000'
 )
 
+# Upload button widget
+upload_image = pn.widgets.Button(name="Upload image", align="end")
+
+############################################
+
+# Organize widgets in a column
 input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
+    """
+    ###### Classify an image with a pre-trained [ResNet18](https://huggingface.co/microsoft/resnet-18) and generate an adversarial example.\n
+
+    Please be patient with the application, it is running on a low-resource device.
+    """,
+    file_input,
+    epsilon_slider,
 )
 
-# add interactivity
+# Add interactivity
 interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
+    pn.bind(
+        process_inputs, upload_image, file_input.param.value, epsilon_slider.param.value
+    ),
     height=600,
 )
 
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
+footer = pn.pane.Markdown(
+    """
+    <br><br><br><br>
+    Wondering where the class names come from? Find the full list [here](https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/)
+    """
+)
 
-# create dashboard
+# Create dashboard
 main = pn.WidgetBox(
     input_widgets,
     interactive_result,
-    footer_row,
+    footer,
 )
 
-title = "Panel Demo - Image Classification"
+title = "Adversarial Sample Generation"
+
 pn.template.BootstrapTemplate(
     title=title,
     main=main,
     main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+    header_background="#101820",
+).servable(title=title)
+
+
+# Functions from original demo
+
+# ICON_URLS = {
+#     "brand-github": "https://github.com/holoviz/panel",
+#     "brand-twitter": "https://twitter.com/Panel_Org",
+#     "brand-linkedin": "https://www.linkedin.com/company/panel-org",
+#     "message-circle": "https://discourse.holoviz.org/",
+#     "brand-discord": "https://discord.gg/AXRHnJU6sP",
+# }
+
+
+# async def random_url(_):
+#     pet = random.choice(["cat", "dog"])
+#     api_url = f"https://api.the{pet}api.com/v1/images/search"
+#     async with aiohttp.ClientSession() as session:
+#         async with session.get(api_url) as resp:
+#             return (await resp.json())[0]["url"]
+
+
+# @pn.cache
+# def load_processor_model(
+#     processor_name: str, model_name: str
+# ) -> Tuple[CLIPProcessor, CLIPModel]:
+#     processor = CLIPProcessor.from_pretrained(processor_name)
+#     model = CLIPModel.from_pretrained(model_name)
+#     return processor, model
+
+
+# async def open_image_url(image_url: str) -> Image:
+#     async with aiohttp.ClientSession() as session:
+#         async with session.get(image_url) as resp:
+#             return Image.open(io.BytesIO(await resp.read()))
+
+
+# def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
+#     processor, model = load_processor_model(
+#         "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
+#     )
+#     inputs = processor(
+#         text=class_items,
+#         images=[image],
+#         return_tensors="pt",  # pytorch tensors
+#     )
+#     print(inputs)
+#     outputs = model(**inputs)
+#     logits_per_image = outputs.logits_per_image
+#     class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
+#     return class_likelihoods[0]

classes.txt

@@ -0,0 +1,1000 @@
+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenter's kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o'-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potter's wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue

requirements.txt

@@ -1,6 +1,10 @@
+--find-links https://download.pytorch.org/whl/cpu
+torch
+torchvision
+# torchaudio
+
 panel
 jupyter
-transformers
+transformers[torch]
 numpy
-torch
 aiohttp