Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+- unknown-6m
+---
+# Model card for nextvit_small.bd_ssld_6m_in1k_384
+
+A Next-ViT image classification model. Trained by paper authors on an unknown 6M sample dataset and ImageNet-1k using SSLD distillation.
+
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 31.8
+  - GMACs: 17.0
+  - Activations (M): 51.7
+  - Image size: 384 x 384
+- **Pretrain Dataset:** Unknown-6M
+- **Dataset:** ImageNet-1k
+- **Papers:**
+  - Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios: https://arxiv.org/abs/2207.05501
+- **Original:** https://github.com/bytedance/Next-ViT
+
+## 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('nextvit_small.bd_ssld_6m_in1k_384', 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(
+    'nextvit_small.bd_ssld_6m_in1k_384',
+    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, 96, 96, 96])
+    #  torch.Size([1, 256, 48, 48])
+    #  torch.Size([1, 512, 24, 24])
+    #  torch.Size([1, 1024, 12, 12])
+
+    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(
+    'nextvit_small.bd_ssld_6m_in1k_384',
+    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, 12, 12) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+### By Top-1
+
+|model                            |top1  |top1_err|top5  |top5_err|param_count|
+|---------------------------------|------|--------|------|--------|-----------|
+|nextvit_large.bd_ssld_6m_in1k_384|86.542|13.458  |98.142|1.858   |57.87      |
+|nextvit_base.bd_ssld_6m_in1k_384 |86.352|13.648  |98.04 |1.96    |44.82      |
+|nextvit_small.bd_ssld_6m_in1k_384|85.964|14.036  |97.908|2.092   |31.76      |
+|nextvit_large.bd_ssld_6m_in1k    |85.48 |14.52   |97.696|2.304   |57.87      |
+|nextvit_base.bd_ssld_6m_in1k     |85.186|14.814  |97.59 |2.41    |44.82      |
+|nextvit_large.bd_in1k_384        |84.924|15.076  |97.294|2.706   |57.87      |
+|nextvit_small.bd_ssld_6m_in1k    |84.862|15.138  |97.382|2.618   |31.76      |
+|nextvit_base.bd_in1k_384         |84.706|15.294  |97.224|2.776   |44.82      |
+|nextvit_small.bd_in1k_384        |84.022|15.978  |96.99 |3.01    |31.76      |
+|nextvit_large.bd_in1k            |83.626|16.374  |96.694|3.306   |57.87      |
+|nextvit_base.bd_in1k             |83.472|16.528  |96.656|3.344   |44.82      |
+|nextvit_small.bd_in1k            |82.61 |17.39   |96.226|3.774   |31.76      |
+
+## Citation
+```bibtex
+@article{li2022next,
+  title={Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios},
+  author={Li, Jiashi and Xia, Xin and Li, Wei and Li, Huixia and Wang, Xing and Xiao, Xuefeng and Wang, Rui and Zheng, Min and Pan, Xin},
+  journal={arXiv preprint arXiv:2207.05501},
+  year={2022}
+}
+```

config.json

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+{
+  "architecture": "nextvit_small",
+  "num_classes": 1000,
+  "num_features": 1024,
+  "pretrained_cfg": {
+    "tag": "bd_ssld_6m_in1k_384",
+    "custom_load": false,
+    "input_size": [
+      3,
+      384,
+      384
+    ],
+    "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": [
+      12,
+      12
+    ],
+    "first_conv": "stem.0.conv",
+    "classifier": "head.fc"
+  }
+}

model.safetensors

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+size 127263832

pytorch_model.bin

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