Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for inception_v3.tf_adv_in1k
+
+A Inception-v3 image classification model. Adversarially trained on ImageNet-1k by paper authors. Ported from Tensorflow by Ross Wightman.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 23.8
+  - GMACs: 5.7
+  - Activations (M): 9.0
+  - Image size: 299 x 299
+- **Papers:**
+  - Rethinking the Inception Architecture for Computer Vision: https://arxiv.org/abs/1512.00567
+  - Adversarial Attacks and Defences Competition: https://arxiv.org/abs/1804.00097
+- **Original:** https://github.com/tensorflow/models
+- **Dataset:** ImageNet-1k
+
+## Model Usage
+### Image Classification
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model('inception_v3.tf_adv_in1k', pretrained=True)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
+```
+
+### Feature Map Extraction
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'inception_v3.tf_adv_in1k',
+    pretrained=True,
+    features_only=True,
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+for o in output:
+    # print shape of each feature map in output
+    # e.g.:
+    #  torch.Size([1, 64, 147, 147])
+    #  torch.Size([1, 192, 71, 71])
+    #  torch.Size([1, 288, 35, 35])
+    #  torch.Size([1, 768, 17, 17])
+    #  torch.Size([1, 2048, 8, 8])
+
+    print(o.shape)
+```
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'inception_v3.tf_adv_in1k',
+    pretrained=True,
+    num_classes=0,  # remove classifier nn.Linear
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+
+# or equivalently (without needing to set num_classes=0)
+
+output = model.forward_features(transforms(img).unsqueeze(0))
+# output is unpooled, a (1, 2048, 8, 8) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
+
+## Citation
+```bibtex
+@article{DBLP:journals/corr/SzegedyVISW15,
+  author    = {Christian Szegedy and
+               Vincent Vanhoucke and
+               Sergey Ioffe and
+               Jonathon Shlens and
+               Zbigniew Wojna},
+  title     = {Rethinking the Inception Architecture for Computer Vision},
+  journal   = {CoRR},
+  volume    = {abs/1512.00567},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1512.00567},
+  archivePrefix = {arXiv},
+  eprint    = {1512.00567},
+  timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+```bibtex
+@article{Kurakin2018AdversarialAA,
+  title={Adversarial Attacks and Defences Competition},
+  author={Alexey Kurakin and Ian J. Goodfellow and Samy Bengio and Yinpeng Dong and Fangzhou Liao and Ming Liang and Tianyu Pang and Jun Zhu and Xiaolin Hu and Cihang Xie and Jianyu Wang and Zhishuai Zhang and Zhou Ren and Alan Loddon Yuille and Sangxia Huang and Yao Zhao and Yuzhe Zhao and Zhonglin Han and Junjiajia Long and Yerkebulan Berdibekov and Takuya Akiba and Seiya Tokui and Motoki Abe},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1804.00097}
+}
+```

config.json

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+{
+  "architecture": "inception_v3",
+  "num_classes": 1000,
+  "num_features": 2048,
+  "pretrained_cfg": {
+    "tag": "tf_adv_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      299,
+      299
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "num_classes": 1000,
+    "label_offset": 1,
+    "pool_size": [
+      8,
+      8
+    ],
+    "first_conv": "Conv2d_1a_3x3.conv",
+    "classifier": "fc"
+  }
+}

model.safetensors

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+size 95533832

pytorch_model.bin

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