Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+- imagenet-12k
+---
+# Model card for mambaout_base_plus_rw.sw_e150_in12k_ft_in1k
+
+A MambaOut image classification model with `timm` specific architecture customizations. Pretrained on ImageNet-12k and fine-tuned on ImageNet-1k by Ross Wightman using Swin / ConvNeXt based recipe.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 101.7
+  - GMACs: 19.2
+  - Activations (M): 45.2
+  - Image size: train = 224 x 224, test = 288 x 288
+- **Pretrain Dataset:** ImageNet-12k
+- **Dataset:** ImageNet-1k
+- **Papers:**
+  - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
+  - MambaOut: Do We Really Need Mamba for Vision?: https://arxiv.org/abs/2405.07992
+- **Original:** https://github.com/yuweihao/MambaOut
+
+## 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('mambaout_base_plus_rw.sw_e150_in12k_ft_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(
+    'mambaout_base_plus_rw.sw_e150_in12k_ft_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, 56, 56, 128])
+    #  torch.Size([1, 28, 28, 256])
+    #  torch.Size([1, 14, 14, 512])
+    #  torch.Size([1, 7, 7, 768])
+
+    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(
+    'mambaout_base_plus_rw.sw_e150_in12k_ft_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, 7, 7, 768) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+### By Top-1
+
+|model                                                                                                                |img_size|top1  |top5  |param_count|
+|---------------------------------------------------------------------------------------------------------------------|--------|------|------|-----------|
+|[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|288     |86.912|98.236|101.66     |
+|[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|224     |86.632|98.156|101.66     |
+|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k)                  |288     |84.974|97.332|86.48      |
+|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k)                  |288     |84.962|97.208|94.45      |
+|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k)                |288     |84.832|97.27 |88.83      |
+|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k)                                                  |288     |84.72 |96.93 |84.81      |
+|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k)                          |288     |84.598|97.098|48.5       |
+|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k)                                                |288     |84.5  |96.974|48.49      |
+|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k)                  |224     |84.454|96.864|94.45      |
+|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k)                  |224     |84.434|96.958|86.48      |
+|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k)                |224     |84.362|96.952|88.83      |
+|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k)                                                  |224     |84.168|96.68 |84.81      |
+|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k)                                                |224     |84.086|96.63 |48.49      |
+|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k)                          |224     |84.024|96.752|48.5       |
+|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k)                                                  |288     |83.448|96.538|26.55      |
+|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k)                                                  |224     |82.736|96.1  |26.55      |
+|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k)                                                  |288     |81.054|95.718|9.14       |
+|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k)                                                  |224     |79.986|94.986|9.14       |
+|[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k)                                                |288     |79.848|95.14 |7.3        |
+|[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k)                                                |224     |78.87 |94.408|7.3        |
+
+## 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
+@article{yu2024mambaout,
+  title={MambaOut: Do We Really Need Mamba for Vision?},
+  author={Yu, Weihao and Wang, Xinchao},
+  journal={arXiv preprint arXiv:2405.07992},
+  year={2024}
+}
+```

config.json

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+{
+  "architecture": "mambaout_base_plus_rw",
+  "num_classes": 1000,
+  "num_features": 768,
+  "pretrained_cfg": {
+    "tag": "sw_e150_in12k_ft_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      224,
+      224
+    ],
+    "test_input_size": [
+      3,
+      288,
+      288
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "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.conv1",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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+size 406664120

pytorch_model.bin

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