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 ese_vovnet57b.ra4_e3600_r256_in1k
+
+A VoVNet-v2 image classification model. Pretrained on ImageNet-1k in `timm` by Ross Wightman using MobileNetV4 inspired RA4 recipe.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 38.6
+  - GMACs: 11.7
+  - Activations (M): 9.8
+  - Image size: train = 256 x 256, test = 320 x 320
+- **Papers:**
+  - An Energy and GPU-Computation Efficient Backbone Network: https://arxiv.org/abs/1904.09730
+  - CenterMask : Real-Time Anchor-Free Instance Segmentation: https://arxiv.org/abs/1911.06667
+  - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
+  - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
+- **Dataset:** ImageNet-1k
+- **Original:** https://github.com/huggingface/pytorch-image-models
+
+## 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('ese_vovnet57b.ra4_e3600_r256_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(
+    'ese_vovnet57b.ra4_e3600_r256_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, 128, 128])
+    #  torch.Size([1, 256, 64, 64])
+    #  torch.Size([1, 512, 32, 32])
+    #  torch.Size([1, 768, 16, 16])
+    #  torch.Size([1, 1024, 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(
+    'ese_vovnet57b.ra4_e3600_r256_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, 1024, 8, 8) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+```bibtex
+@inproceedings{lee2019energy,
+  title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
+  author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul},
+  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
+  year = {2019}
+}
+```
+```bibtex
+@article{lee2019centermask,
+  title={CenterMask: Real-Time Anchor-Free Instance Segmentation},
+  author={Lee, Youngwan and Park, Jongyoul},
+  booktitle={CVPR},
+  year={2020}
+}
+```
+```bibtex
+@inproceedings{wightman2021resnet,
+  title={ResNet strikes back: An improved training procedure in timm},
+  author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
+  booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
+}
+```
+```bibtex
+@article{qin2024mobilenetv4,
+  title={MobileNetV4-Universal Models for the Mobile Ecosystem},
+  author={Qin, Danfeng and Leichner, Chas and Delakis, Manolis and Fornoni, Marco and Luo, Shixin and Yang, Fan and Wang, Weijun and Banbury, Colby and Ye, Chengxi and Akin, Berkin and others},
+  journal={arXiv preprint arXiv:2404.10518},
+  year={2024}
+}
+```

config.json

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+{
+  "architecture": "ese_vovnet57b",
+  "num_classes": 1000,
+  "num_features": 1024,
+  "pretrained_cfg": {
+    "tag": "ra4_e3600_r256_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      256,
+      256
+    ],
+    "test_input_size": [
+      3,
+      320,
+      320
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 0.95,
+    "test_crop_pct": 1.0,
+    "crop_mode": "center",
+    "mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      8,
+      8
+    ],
+    "first_conv": "stem.0.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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pytorch_model.bin

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