Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_tag: timm
+license: mit
+datasets:
+- imagenet-1k
+---
+# Model card for repvgg_a2.rvgg_in1k
+
+A RepVGG image classification model. Trained on ImageNet-1k by paper authors.
+
+This model architecture is implemented using `timm`'s flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py).
+
+BYOBNet allows configuration of:
+ * block / stage layout
+ * stem layout
+ * output stride (dilation)
+ * activation and norm layers
+ * channel and spatial / self-attention layers
+
+...and also includes `timm` features common to many other architectures, including:
+ * stochastic depth
+ * gradient checkpointing
+ * layer-wise LR decay
+ * per-stage feature extraction
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 28.2
+  - GMACs: 5.7
+  - Activations (M): 6.3
+  - Image size: 224 x 224
+- **Papers:**
+  - RepVGG: Making VGG-style ConvNets Great Again: https://arxiv.org/abs/2101.03697
+- **Dataset:** ImageNet-1k
+- **Original:** https://github.com/DingXiaoH/RepVGG
+
+## 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('repvgg_a2.rvgg_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(
+    'repvgg_a2.rvgg_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, 112, 112])
+    #  torch.Size([1, 96, 56, 56])
+    #  torch.Size([1, 192, 28, 28])
+    #  torch.Size([1, 384, 14, 14])
+    #  torch.Size([1, 1408, 7, 7])
+
+    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(
+    'repvgg_a2.rvgg_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, 1408, 7, 7) 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
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
+}
+```
+```bibtex
+@inproceedings{ding2021repvgg,
+  title={Repvgg: Making vgg-style convnets great again},
+  author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={13733--13742},
+  year={2021}
+}
+```

config.json

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+{
+  "architecture": "repvgg_a2",
+  "num_classes": 1000,
+  "num_features": 1408,
+  "pretrained_cfg": {
+    "tag": "rvgg_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bilinear",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": [
+      "stem.conv_kxk.conv",
+      "stem.conv_1x1.conv"
+    ],
+    "classifier": "head.fc",
+    "license": "mit"
+  }
+}

model.safetensors

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pytorch_model.bin

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