Upload folder using huggingface_hub
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- .gitattributes +36 -35
- README.md +12 -12
- app.py +309 -0
- data/Imagenette.txt +10 -0
- data/Imagenette/val/n01440764/ILSVRC2012_val_00009111.JPEG +0 -0
- data/Imagenette/val/n01440764/ILSVRC2012_val_00009191.JPEG +0 -0
- data/Imagenette/val/n02102040/ILSVRC2012_val_00004650.JPEG +0 -0
- data/Imagenette/val/n02102040/ILSVRC2012_val_00007032.JPEG +0 -0
- data/Imagenette/val/n02979186/ILSVRC2012_val_00008651.JPEG +0 -0
- data/Imagenette/val/n02979186/ILSVRC2012_val_00020400.JPEG +0 -0
- data/Imagenette/val/n03000684/ILSVRC2012_val_00004262.JPEG +0 -0
- data/Imagenette/val/n03000684/ILSVRC2012_val_00007460.JPEG +0 -0
- data/Imagenette/val/n03028079/ILSVRC2012_val_00003351.JPEG +0 -0
- data/Imagenette/val/n03028079/ILSVRC2012_val_00003682.JPEG +0 -0
- data/Imagenette/val/n03394916/ILSVRC2012_val_00001492.JPEG +0 -0
- data/Imagenette/val/n03394916/ILSVRC2012_val_00003620.JPEG +0 -0
- data/Imagenette/val/n03417042/ILSVRC2012_val_00002210.JPEG +0 -0
- data/Imagenette/val/n03417042/ILSVRC2012_val_00006922.JPEG +0 -0
- data/Imagenette/val/n03425413/ILSVRC2012_val_00000732.JPEG +0 -0
- data/Imagenette/val/n03425413/ILSVRC2012_val_00001432.JPEG +0 -0
- data/Imagenette/val/n03445777/ILSVRC2012_val_00008161.JPEG +0 -0
- data/Imagenette/val/n03445777/ILSVRC2012_val_00009902.JPEG +0 -0
- data/Imagenette/val/n03888257/ILSVRC2012_val_00001440.JPEG +0 -0
- data/Imagenette/val/n03888257/ILSVRC2012_val_00002990.JPEG +0 -0
- data/Imagewoof.txt +10 -0
- data/Imagewoof/val/n02086240/ILSVRC2012_val_00002701.JPEG +0 -0
- data/Imagewoof/val/n02086240/ILSVRC2012_val_00003841.JPEG +0 -0
- data/Imagewoof/val/n02087394/ILSVRC2012_val_00000102.JPEG +0 -0
- data/Imagewoof/val/n02087394/ILSVRC2012_val_00001651.JPEG +0 -0
- data/Imagewoof/val/n02088364/ILSVRC2012_val_00005291.JPEG +0 -0
- data/Imagewoof/val/n02088364/ILSVRC2012_val_00005922.JPEG +0 -0
- data/Imagewoof/val/n02089973/ILSVRC2012_val_00003671.JPEG +0 -0
- data/Imagewoof/val/n02089973/ILSVRC2012_val_00007850.JPEG +0 -0
- data/Imagewoof/val/n02093754/ILSVRC2012_val_00000832.JPEG +0 -0
- data/Imagewoof/val/n02093754/ILSVRC2012_val_00004511.JPEG +0 -0
- data/Imagewoof/val/n02096294/ILSVRC2012_val_00003690.JPEG +0 -0
- data/Imagewoof/val/n02096294/ILSVRC2012_val_00009052.JPEG +0 -0
- data/Imagewoof/val/n02099601/ILSVRC2012_val_00001112.JPEG +0 -0
- data/Imagewoof/val/n02099601/ILSVRC2012_val_00001191.JPEG +0 -0
- data/Imagewoof/val/n02105641/ILSVRC2012_val_00004361.JPEG +0 -0
- data/Imagewoof/val/n02105641/ILSVRC2012_val_00005390.JPEG +0 -0
- data/Imagewoof/val/n02111889/ILSVRC2012_val_00000590.JPEG +0 -0
- data/Imagewoof/val/n02111889/ILSVRC2012_val_00003490.JPEG +0 -0
- data/Imagewoof/val/n02115641/ILSVRC2012_val_00001212.JPEG +0 -0
- data/Imagewoof/val/n02115641/ILSVRC2012_val_00004320.JPEG +0 -0
- data/Stanford_dogs.txt +120 -0
- data/Stanford_dogs/val/n02085620-Chihuahua/n02085620_10074.jpg +0 -0
- data/Stanford_dogs/val/n02085620-Chihuahua/n02085620_10131.jpg +0 -0
- data/Stanford_dogs/val/n02085782-Japanese_spaniel/n02085782_1039.jpg +0 -0
- data/Stanford_dogs/val/n02085782-Japanese_spaniel/n02085782_1058.jpg +0 -0
.gitattributes
CHANGED
@@ -1,35 +1,36 @@
|
|
1 |
-
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
-
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
-
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
-
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
-
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
-
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
-
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
-
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
-
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
-
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
-
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
-
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
-
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
-
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
-
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
-
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
-
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
-
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
-
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
-
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
-
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
-
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
-
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
-
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
-
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
-
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
-
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
-
*.tar filter=lfs diff=lfs merge=lfs -text
|
29 |
-
*.tflite filter=lfs diff=lfs merge=lfs -text
|
30 |
-
*.tgz filter=lfs diff=lfs merge=lfs -text
|
31 |
-
*.wasm filter=lfs diff=lfs merge=lfs -text
|
32 |
-
*.xz filter=lfs diff=lfs merge=lfs -text
|
33 |
-
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
-
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
-
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
36 |
+
data/Stanford_dogs/val/n02093859-Kerry_blue_terrier/n02093859_1003.jpg filter=lfs diff=lfs merge=lfs -text
|
README.md
CHANGED
@@ -1,12 +1,12 @@
|
|
1 |
-
---
|
2 |
-
title: Generative Augmented Classifiers
|
3 |
-
emoji:
|
4 |
-
colorFrom:
|
5 |
-
colorTo:
|
6 |
-
sdk: gradio
|
7 |
-
sdk_version: 4.36.1
|
8 |
-
app_file: app.py
|
9 |
-
pinned: false
|
10 |
-
---
|
11 |
-
|
12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
1 |
+
---
|
2 |
+
title: Generative Augmented Classifiers
|
3 |
+
emoji: 💻
|
4 |
+
colorFrom: gray
|
5 |
+
colorTo: indigo
|
6 |
+
sdk: gradio
|
7 |
+
sdk_version: 4.36.1
|
8 |
+
app_file: app.py
|
9 |
+
pinned: false
|
10 |
+
---
|
11 |
+
|
12 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
@@ -0,0 +1,309 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import random
|
2 |
+
|
3 |
+
import gradio as gr
|
4 |
+
import torch
|
5 |
+
import torchvision
|
6 |
+
import torchvision.transforms as transforms
|
7 |
+
from PIL import Image
|
8 |
+
from torch import nn
|
9 |
+
from torchvision.models import mobilenet_v2, resnet18
|
10 |
+
from torchvision.transforms.functional import InterpolationMode
|
11 |
+
|
12 |
+
datasets_n_classes = {
|
13 |
+
"Imagenette": 10,
|
14 |
+
"Imagewoof": 10,
|
15 |
+
"Stanford_dogs": 120,
|
16 |
+
}
|
17 |
+
|
18 |
+
datasets_model_types = {
|
19 |
+
"Imagenette": [
|
20 |
+
"base_200",
|
21 |
+
"base_200+100",
|
22 |
+
"synthetic_200",
|
23 |
+
"augment_noisy_200",
|
24 |
+
"augment_noisy_200+100",
|
25 |
+
"augment_clean_200",
|
26 |
+
],
|
27 |
+
"Imagewoof": [
|
28 |
+
"base_200",
|
29 |
+
"base_200+100",
|
30 |
+
"synthetic_200",
|
31 |
+
"augment_noisy_200",
|
32 |
+
"augment_noisy_200+100",
|
33 |
+
"augment_clean_200",
|
34 |
+
],
|
35 |
+
"Stanford_dogs": [
|
36 |
+
"base_200",
|
37 |
+
"base_200+100",
|
38 |
+
"synthetic_200",
|
39 |
+
"augment_noisy_200",
|
40 |
+
"augment_noisy_200+100",
|
41 |
+
],
|
42 |
+
}
|
43 |
+
|
44 |
+
model_arch = ["resnet18", "mobilenet_v2"]
|
45 |
+
|
46 |
+
list_200 = [
|
47 |
+
"Original",
|
48 |
+
"Synthetic",
|
49 |
+
"Original + Synthetic (Noisy)",
|
50 |
+
"Original + Synthetic (Clean)",
|
51 |
+
]
|
52 |
+
|
53 |
+
list_200_100 = ["Base+100", "AugmentNoisy+100"]
|
54 |
+
|
55 |
+
methods_map = {
|
56 |
+
"200 Epochs": list_200,
|
57 |
+
"200 Epochs on Original + 100": list_200_100,
|
58 |
+
}
|
59 |
+
|
60 |
+
label_map = dict()
|
61 |
+
label_map["Imagenette (10 classes)"] = "Imagenette"
|
62 |
+
label_map["Imagewoof (10 classes)"] = "Imagewoof"
|
63 |
+
label_map["Stanford Dogs (120 classes)"] = "Stanford_dogs"
|
64 |
+
label_map["ResNet-18"] = "resnet18"
|
65 |
+
label_map["MobileNetV2"] = "mobilenet_v2"
|
66 |
+
label_map["200 Epochs"] = "200"
|
67 |
+
label_map["200 Epochs on Original + 100"] = "200+100"
|
68 |
+
label_map["Original"] = "base"
|
69 |
+
label_map["Synthetic"] = "synthetic"
|
70 |
+
label_map["Original + Synthetic (Noisy)"] = "augment_noisy"
|
71 |
+
label_map["Original + Synthetic (Clean)"] = "augment_clean"
|
72 |
+
label_map["Base+100"] = "base"
|
73 |
+
label_map["AugmentNoisy+100"] = "augment_noisy"
|
74 |
+
|
75 |
+
dataset_models = dict()
|
76 |
+
for dataset, n_classes in datasets_n_classes.items():
|
77 |
+
models = dict()
|
78 |
+
for model_type in datasets_model_types[dataset]:
|
79 |
+
for arch in model_arch:
|
80 |
+
if arch == "resnet18":
|
81 |
+
model = resnet18(weights=None, num_classes=n_classes)
|
82 |
+
models[f"{arch}_{model_type}"] = (
|
83 |
+
model,
|
84 |
+
f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
|
85 |
+
)
|
86 |
+
elif arch == "mobilenet_v2":
|
87 |
+
model = mobilenet_v2(weights=None, num_classes=n_classes)
|
88 |
+
models[f"{arch}_{model_type}"] = (
|
89 |
+
model,
|
90 |
+
f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
|
91 |
+
)
|
92 |
+
else:
|
93 |
+
raise ValueError(f"Model architecture unavailable: {arch}")
|
94 |
+
dataset_models[dataset] = models
|
95 |
+
|
96 |
+
|
97 |
+
def get_random_image(dataset, label_map=label_map) -> Image:
|
98 |
+
dataset_root = f"./data/{label_map[dataset]}/val"
|
99 |
+
dataset_img = torchvision.datasets.ImageFolder(
|
100 |
+
dataset_root,
|
101 |
+
transforms.Compose([transforms.PILToTensor()]),
|
102 |
+
)
|
103 |
+
random_idx = random.randint(0, len(dataset_img) - 1)
|
104 |
+
image, _ = dataset_img[random_idx]
|
105 |
+
image = transforms.ToPILImage()(image)
|
106 |
+
image = image.resize(
|
107 |
+
(256, 256),
|
108 |
+
)
|
109 |
+
return image
|
110 |
+
|
111 |
+
|
112 |
+
def load_model(model_dict, model_name: str) -> nn.Module:
|
113 |
+
model_name_lower = model_name.lower()
|
114 |
+
if model_name_lower in model_dict:
|
115 |
+
model = model_dict[model_name_lower][0]
|
116 |
+
model_path = model_dict[model_name_lower][1]
|
117 |
+
checkpoint = torch.load(model_path)
|
118 |
+
if "setup" in checkpoint:
|
119 |
+
if checkpoint["setup"]["distributed"]:
|
120 |
+
torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(
|
121 |
+
checkpoint["model"], "module."
|
122 |
+
)
|
123 |
+
model.load_state_dict(checkpoint["model"])
|
124 |
+
else:
|
125 |
+
model.load_state_dict(checkpoint)
|
126 |
+
return model
|
127 |
+
else:
|
128 |
+
raise ValueError(
|
129 |
+
f"Model {model_name} is not available for image prediction. Please choose from {[name.capitalize() for name in model_dict.keys()]}."
|
130 |
+
)
|
131 |
+
|
132 |
+
|
133 |
+
def postprocess_default(labels, output) -> dict:
|
134 |
+
probabilities = nn.functional.softmax(output[0], dim=0)
|
135 |
+
top_prob, top_catid = torch.topk(probabilities, 5)
|
136 |
+
confidences = {
|
137 |
+
labels[top_catid.tolist()[i]]: top_prob.tolist()[i]
|
138 |
+
for i in range(top_prob.shape[0])
|
139 |
+
}
|
140 |
+
return confidences
|
141 |
+
|
142 |
+
|
143 |
+
def classify(
|
144 |
+
input_image: Image,
|
145 |
+
dataset_type: str,
|
146 |
+
arch_type: str,
|
147 |
+
methods: str,
|
148 |
+
training_ds: str,
|
149 |
+
dataset_models=dataset_models,
|
150 |
+
label_map=label_map,
|
151 |
+
) -> dict:
|
152 |
+
for i in [dataset_type, arch_type, methods, training_ds]:
|
153 |
+
if i is None:
|
154 |
+
raise ValueError("Please select all options.")
|
155 |
+
dataset_type = label_map[dataset_type]
|
156 |
+
arch_type = label_map[arch_type]
|
157 |
+
methods = label_map[methods]
|
158 |
+
training_ds = label_map[training_ds]
|
159 |
+
preprocess_input = transforms.Compose(
|
160 |
+
[
|
161 |
+
transforms.Resize(
|
162 |
+
256,
|
163 |
+
interpolation=InterpolationMode.BILINEAR,
|
164 |
+
antialias=True,
|
165 |
+
),
|
166 |
+
transforms.CenterCrop(224),
|
167 |
+
transforms.ToTensor(),
|
168 |
+
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
169 |
+
]
|
170 |
+
)
|
171 |
+
if input_image is None:
|
172 |
+
raise ValueError("No image was provided.")
|
173 |
+
input_tensor: torch.Tensor = preprocess_input(input_image)
|
174 |
+
input_batch = input_tensor.unsqueeze(0)
|
175 |
+
model = load_model(
|
176 |
+
dataset_models[dataset_type], f"{arch_type}_{training_ds}_{methods}"
|
177 |
+
)
|
178 |
+
|
179 |
+
if torch.cuda.is_available():
|
180 |
+
input_batch = input_batch.to("cuda")
|
181 |
+
model.to("cuda")
|
182 |
+
|
183 |
+
model.eval()
|
184 |
+
with torch.inference_mode():
|
185 |
+
output: torch.Tensor = model(input_batch)
|
186 |
+
with open(f"./data/{dataset_type}.txt", "r") as f:
|
187 |
+
labels = {i: line.strip() for i, line in enumerate(f.readlines())}
|
188 |
+
return postprocess_default(labels, output)
|
189 |
+
|
190 |
+
|
191 |
+
def update_methods(method, ds_type):
|
192 |
+
if ds_type == "Stanford Dogs (120 classes)" and method == "200 Epochs":
|
193 |
+
methods = list_200[:-1]
|
194 |
+
else:
|
195 |
+
methods = methods_map[method]
|
196 |
+
return gr.update(choices=methods, value=None)
|
197 |
+
|
198 |
+
|
199 |
+
def downloadModel(
|
200 |
+
dataset_type, arch_type, methods, training_ds, dataset_models=dataset_models
|
201 |
+
):
|
202 |
+
for i in [dataset_type, arch_type, methods, training_ds]:
|
203 |
+
if i is None:
|
204 |
+
return gr.update(label="Select Model", value=None)
|
205 |
+
dataset_type = label_map[dataset_type]
|
206 |
+
arch_type = label_map[arch_type]
|
207 |
+
methods = label_map[methods]
|
208 |
+
training_ds = label_map[training_ds]
|
209 |
+
if f"{arch_type}_{training_ds}_{methods}" not in dataset_models[dataset_type]:
|
210 |
+
return gr.update(label="Select Model", value=None)
|
211 |
+
model_path = dataset_models[dataset_type][f"{arch_type}_{training_ds}_{methods}"][1]
|
212 |
+
return gr.update(
|
213 |
+
label=f"Download Model: '{dataset_type}_{arch_type}_{training_ds}_{methods}'",
|
214 |
+
value=model_path,
|
215 |
+
)
|
216 |
+
|
217 |
+
|
218 |
+
if __name__ == "__main__":
|
219 |
+
with gr.Blocks(title="Generative Augmented Image Classifiers") as demo:
|
220 |
+
gr.Markdown(
|
221 |
+
"""
|
222 |
+
# Generative Augmented Image Classifiers
|
223 |
+
This demo showcases the performance of image classifiers trained on various datasets.
|
224 |
+
"""
|
225 |
+
)
|
226 |
+
with gr.Row():
|
227 |
+
with gr.Column():
|
228 |
+
dataset_type = gr.Radio(
|
229 |
+
choices=[
|
230 |
+
"Imagenette (10 classes)",
|
231 |
+
"Imagewoof (10 classes)",
|
232 |
+
"Stanford Dogs (120 classes)",
|
233 |
+
],
|
234 |
+
label="Dataset",
|
235 |
+
value="Imagenette (10 classes)",
|
236 |
+
)
|
237 |
+
arch_type = gr.Radio(
|
238 |
+
choices=["ResNet-18", "MobileNetV2"],
|
239 |
+
label="Model Architecture",
|
240 |
+
value="ResNet-18",
|
241 |
+
interactive=True,
|
242 |
+
)
|
243 |
+
methods = gr.Radio(
|
244 |
+
label="Methods",
|
245 |
+
choices=["200 Epochs", "200 Epochs on Original + 100"],
|
246 |
+
interactive=True,
|
247 |
+
value="200 Epochs",
|
248 |
+
)
|
249 |
+
training_ds = gr.Radio(
|
250 |
+
label="Training Dataset",
|
251 |
+
choices=methods_map["200 Epochs"],
|
252 |
+
interactive=True,
|
253 |
+
value="Original",
|
254 |
+
)
|
255 |
+
dataset_type.change(
|
256 |
+
fn=update_methods,
|
257 |
+
inputs=[methods, dataset_type],
|
258 |
+
outputs=[training_ds],
|
259 |
+
)
|
260 |
+
methods.change(
|
261 |
+
fn=update_methods,
|
262 |
+
inputs=[methods, dataset_type],
|
263 |
+
outputs=[training_ds],
|
264 |
+
)
|
265 |
+
generate_button = gr.Button("Sample Random Image")
|
266 |
+
random_image_output = gr.Image(
|
267 |
+
type="pil", label="Random Image from Validation Set"
|
268 |
+
)
|
269 |
+
classify_button_random = gr.Button("Classify")
|
270 |
+
with gr.Column():
|
271 |
+
output_label_random = gr.Label(num_top_classes=5)
|
272 |
+
download_model = gr.DownloadButton(
|
273 |
+
label=f"Download Model: '{label_map[dataset_type.value]}_{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}'",
|
274 |
+
value=dataset_models[label_map[dataset_type.value]][
|
275 |
+
f"{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}"
|
276 |
+
][1],
|
277 |
+
)
|
278 |
+
dataset_type.change(
|
279 |
+
fn=downloadModel,
|
280 |
+
inputs=[dataset_type, arch_type, methods, training_ds],
|
281 |
+
outputs=[download_model],
|
282 |
+
)
|
283 |
+
arch_type.change(
|
284 |
+
fn=downloadModel,
|
285 |
+
inputs=[dataset_type, arch_type, methods, training_ds],
|
286 |
+
outputs=[download_model],
|
287 |
+
)
|
288 |
+
methods.change(
|
289 |
+
fn=downloadModel,
|
290 |
+
inputs=[dataset_type, arch_type, methods, training_ds],
|
291 |
+
outputs=[download_model],
|
292 |
+
)
|
293 |
+
training_ds.change(
|
294 |
+
fn=downloadModel,
|
295 |
+
inputs=[dataset_type, arch_type, methods, training_ds],
|
296 |
+
outputs=[download_model],
|
297 |
+
)
|
298 |
+
|
299 |
+
generate_button.click(
|
300 |
+
get_random_image,
|
301 |
+
inputs=[dataset_type],
|
302 |
+
outputs=random_image_output,
|
303 |
+
)
|
304 |
+
classify_button_random.click(
|
305 |
+
classify,
|
306 |
+
inputs=[random_image_output, dataset_type, arch_type, methods, training_ds],
|
307 |
+
outputs=output_label_random,
|
308 |
+
)
|
309 |
+
demo.launch(show_error=True)
|
data/Imagenette.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
tench, Tinca tinca
|
2 |
+
English springer, English springer spaniel
|
3 |
+
cassette player
|
4 |
+
chain saw, chainsaw
|
5 |
+
church, church building
|
6 |
+
French horn, horn
|
7 |
+
garbage truck, dustcart
|
8 |
+
gas pump, gasoline pump, petrol pump, island dispenser
|
9 |
+
golf ball
|
10 |
+
parachute, chute
|
data/Imagenette/val/n01440764/ILSVRC2012_val_00009111.JPEG
ADDED
|
data/Imagenette/val/n01440764/ILSVRC2012_val_00009191.JPEG
ADDED
|
data/Imagenette/val/n02102040/ILSVRC2012_val_00004650.JPEG
ADDED
|
data/Imagenette/val/n02102040/ILSVRC2012_val_00007032.JPEG
ADDED
|
data/Imagenette/val/n02979186/ILSVRC2012_val_00008651.JPEG
ADDED
|
data/Imagenette/val/n02979186/ILSVRC2012_val_00020400.JPEG
ADDED
|
data/Imagenette/val/n03000684/ILSVRC2012_val_00004262.JPEG
ADDED
|
data/Imagenette/val/n03000684/ILSVRC2012_val_00007460.JPEG
ADDED
|
data/Imagenette/val/n03028079/ILSVRC2012_val_00003351.JPEG
ADDED
|
data/Imagenette/val/n03028079/ILSVRC2012_val_00003682.JPEG
ADDED
|
data/Imagenette/val/n03394916/ILSVRC2012_val_00001492.JPEG
ADDED
|
data/Imagenette/val/n03394916/ILSVRC2012_val_00003620.JPEG
ADDED
|
data/Imagenette/val/n03417042/ILSVRC2012_val_00002210.JPEG
ADDED
|
data/Imagenette/val/n03417042/ILSVRC2012_val_00006922.JPEG
ADDED
|
data/Imagenette/val/n03425413/ILSVRC2012_val_00000732.JPEG
ADDED
|
data/Imagenette/val/n03425413/ILSVRC2012_val_00001432.JPEG
ADDED
|
data/Imagenette/val/n03445777/ILSVRC2012_val_00008161.JPEG
ADDED
|
data/Imagenette/val/n03445777/ILSVRC2012_val_00009902.JPEG
ADDED
|
data/Imagenette/val/n03888257/ILSVRC2012_val_00001440.JPEG
ADDED
|
data/Imagenette/val/n03888257/ILSVRC2012_val_00002990.JPEG
ADDED
|
data/Imagewoof.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Shih-Tzu
|
2 |
+
Rhodesian ridgeback
|
3 |
+
beagle
|
4 |
+
English foxhound
|
5 |
+
Border terrier
|
6 |
+
Australian terrier
|
7 |
+
golden retriever
|
8 |
+
Old English sheepdog, bobtail
|
9 |
+
Samoyed, Samoyede
|
10 |
+
dingo, warrigal, warragal, Canis dingo
|
data/Imagewoof/val/n02086240/ILSVRC2012_val_00002701.JPEG
ADDED
|
data/Imagewoof/val/n02086240/ILSVRC2012_val_00003841.JPEG
ADDED
|
data/Imagewoof/val/n02087394/ILSVRC2012_val_00000102.JPEG
ADDED
|
data/Imagewoof/val/n02087394/ILSVRC2012_val_00001651.JPEG
ADDED
|
data/Imagewoof/val/n02088364/ILSVRC2012_val_00005291.JPEG
ADDED
|
data/Imagewoof/val/n02088364/ILSVRC2012_val_00005922.JPEG
ADDED
|
data/Imagewoof/val/n02089973/ILSVRC2012_val_00003671.JPEG
ADDED
|
data/Imagewoof/val/n02089973/ILSVRC2012_val_00007850.JPEG
ADDED
|
data/Imagewoof/val/n02093754/ILSVRC2012_val_00000832.JPEG
ADDED
|
data/Imagewoof/val/n02093754/ILSVRC2012_val_00004511.JPEG
ADDED
|
data/Imagewoof/val/n02096294/ILSVRC2012_val_00003690.JPEG
ADDED
|
data/Imagewoof/val/n02096294/ILSVRC2012_val_00009052.JPEG
ADDED
|
data/Imagewoof/val/n02099601/ILSVRC2012_val_00001112.JPEG
ADDED
|
data/Imagewoof/val/n02099601/ILSVRC2012_val_00001191.JPEG
ADDED
|
data/Imagewoof/val/n02105641/ILSVRC2012_val_00004361.JPEG
ADDED
|
data/Imagewoof/val/n02105641/ILSVRC2012_val_00005390.JPEG
ADDED
|
data/Imagewoof/val/n02111889/ILSVRC2012_val_00000590.JPEG
ADDED
|
data/Imagewoof/val/n02111889/ILSVRC2012_val_00003490.JPEG
ADDED
|
data/Imagewoof/val/n02115641/ILSVRC2012_val_00001212.JPEG
ADDED
|
data/Imagewoof/val/n02115641/ILSVRC2012_val_00004320.JPEG
ADDED
|
data/Stanford_dogs.txt
ADDED
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Chihuahua
|
2 |
+
Japanese spaniel
|
3 |
+
Maltese dog
|
4 |
+
Pekinese
|
5 |
+
Shih Tzu
|
6 |
+
Blenheim spaniel
|
7 |
+
papillon
|
8 |
+
toy terrier
|
9 |
+
Rhodesian ridgeback
|
10 |
+
Afghan hound
|
11 |
+
basset
|
12 |
+
beagle
|
13 |
+
bloodhound
|
14 |
+
bluetick
|
15 |
+
black and tan coonhound
|
16 |
+
Walker hound
|
17 |
+
English foxhound
|
18 |
+
redbone
|
19 |
+
borzoi
|
20 |
+
Irish wolfhound
|
21 |
+
Italian greyhound
|
22 |
+
whippet
|
23 |
+
Ibizan hound
|
24 |
+
Norwegian elkhound
|
25 |
+
otterhound
|
26 |
+
Saluki
|
27 |
+
Scottish deerhound
|
28 |
+
Weimaraner
|
29 |
+
Staffordshire bullterrier
|
30 |
+
American Staffordshire terrier
|
31 |
+
Bedlington terrier
|
32 |
+
Border terrier
|
33 |
+
Kerry blue terrier
|
34 |
+
Irish terrier
|
35 |
+
Norfolk terrier
|
36 |
+
Norwich terrier
|
37 |
+
Yorkshire terrier
|
38 |
+
wire haired fox terrier
|
39 |
+
Lakeland terrier
|
40 |
+
Sealyham terrier
|
41 |
+
Airedale
|
42 |
+
cairn
|
43 |
+
Australian terrier
|
44 |
+
Dandie Dinmont
|
45 |
+
Boston bull
|
46 |
+
miniature schnauzer
|
47 |
+
giant schnauzer
|
48 |
+
standard schnauzer
|
49 |
+
Scotch terrier
|
50 |
+
Tibetan terrier
|
51 |
+
silky terrier
|
52 |
+
soft coated wheaten terrier
|
53 |
+
West Highland white terrier
|
54 |
+
Lhasa
|
55 |
+
flat coated retriever
|
56 |
+
curly coated retriever
|
57 |
+
golden retriever
|
58 |
+
Labrador retriever
|
59 |
+
Chesapeake Bay retriever
|
60 |
+
German short haired pointer
|
61 |
+
vizsla
|
62 |
+
English setter
|
63 |
+
Irish setter
|
64 |
+
Gordon setter
|
65 |
+
Brittany spaniel
|
66 |
+
clumber
|
67 |
+
English springer
|
68 |
+
Welsh springer spaniel
|
69 |
+
cocker spaniel
|
70 |
+
Sussex spaniel
|
71 |
+
Irish water spaniel
|
72 |
+
kuvasz
|
73 |
+
schipperke
|
74 |
+
groenendael
|
75 |
+
malinois
|
76 |
+
briard
|
77 |
+
kelpie
|
78 |
+
komondor
|
79 |
+
Old English sheepdog
|
80 |
+
Shetland sheepdog
|
81 |
+
collie
|
82 |
+
Border collie
|
83 |
+
Bouvier des Flandres
|
84 |
+
Rottweiler
|
85 |
+
German shepherd
|
86 |
+
Doberman
|
87 |
+
miniature pinscher
|
88 |
+
Greater Swiss Mountain dog
|
89 |
+
Bernese mountain dog
|
90 |
+
Appenzeller
|
91 |
+
EntleBucher
|
92 |
+
boxer
|
93 |
+
bull mastiff
|
94 |
+
Tibetan mastiff
|
95 |
+
French bulldog
|
96 |
+
Great Dane
|
97 |
+
Saint Bernard
|
98 |
+
Eskimo dog
|
99 |
+
malamute
|
100 |
+
Siberian husky
|
101 |
+
affenpinscher
|
102 |
+
basenji
|
103 |
+
pug
|
104 |
+
Leonberg
|
105 |
+
Newfoundland
|
106 |
+
Great Pyrenees
|
107 |
+
Samoyed
|
108 |
+
Pomeranian
|
109 |
+
chow
|
110 |
+
keeshond
|
111 |
+
Brabancon griffon
|
112 |
+
Pembroke
|
113 |
+
Cardigan
|
114 |
+
toy poodle
|
115 |
+
miniature poodle
|
116 |
+
standard poodle
|
117 |
+
Mexican hairless
|
118 |
+
dingo
|
119 |
+
dhole
|
120 |
+
African hunting dog
|
data/Stanford_dogs/val/n02085620-Chihuahua/n02085620_10074.jpg
ADDED
![]() |
data/Stanford_dogs/val/n02085620-Chihuahua/n02085620_10131.jpg
ADDED
![]() |
data/Stanford_dogs/val/n02085782-Japanese_spaniel/n02085782_1039.jpg
ADDED
![]() |
data/Stanford_dogs/val/n02085782-Japanese_spaniel/n02085782_1058.jpg
ADDED
![]() |