Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_tag: timm
+license: cc-by-nc-4.0
+---
+# Model card for convnextv2_nano.fcmae
+
+A ConvNeXt-V2 self-supervised feature representation model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE). This model has no pretrained head and is only useful for fine-tune or feature extraction.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Paper:** [**ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders**](http://arxiv.org/abs/2301.00808)
+
+## Citation
+```
+@article{Woo2023ConvNeXtV2,
+  title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
+  author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
+  year={2023},
+  journal={arXiv preprint arXiv:2301.00808},
+}
+```

config.json

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+{
+  "architecture": "convnextv2_nano",
+  "num_classes": 0,
+  "num_features": 640,
+  "pretrained_cfg": {
+    "tag": "fcmae",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 0.875,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 0,
+    "pool_size": [
+      7,
+      7
+    ],
+    "first_conv": "stem.0",
+    "classifier": "head.fc",
+    "license": "cc-by-nc-4.0",
+    "origin_url": "https://github.com/facebookresearch/ConvNeXt-V2",
+    "paper_name": "ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders",
+    "paper_ids": "arXiv:2301.00808"
+  }
+}

pytorch_model.bin

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+size 59983293