Add model

README.md

@@ -0,0 +1,160 @@
+---
+tags:
+- image-feature-extraction
+- timm
+- transformers
+pipeline_tag: image-feature-extraction
+library_name: timm
+license: fair-noncommercial-research-license
+datasets:
+- lvd-1689m
+---
+# Model card for convnext_small.eupe_lvd1689m
+
+An EUPE ConvNeXt image feature encoder. Distilled on LVD-1689M using the Efficient Universal Perception Encoder method, from a proxy teacher distilled from multiple domain-expert foundation vision encoders.
+
+
+## Model Details
+- **Model Type:** Image Feature Encoder
+- **Model Stats:**
+  - Params (M): 49.5
+  - GMACs: 11.4
+  - Activations (M): 28.2
+  - Image size: 256 x 256
+- **Original:** https://github.com/facebookresearch/EUPE
+- **License:** [FAIR Noncommercial Research License](https://huggingface.co/facebook/fair-noncommercial-research-license/)
+- **Dataset:** LVD-1689M
+- **Papers:**
+  - Efficient Universal Perception Encoder: https://arxiv.org/abs/2603.22387
+  - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
+  - PyTorch Image Models: 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('convnext_small.eupe_lvd1689m', 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(
+    'convnext_small.eupe_lvd1689m',
+    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, 64, 64])
+    #  torch.Size([1, 192, 32, 32])
+    #  torch.Size([1, 384, 16, 16])
+    #  torch.Size([1, 768, 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(
+    'convnext_small.eupe_lvd1689m',
+    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, 768, 8, 8) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+See the associated paper for details on the evaluation protocols.
+
+| Model | Params | TextVQA | SQA | Realworld | POPE | GQA | MMEp | SPair | NYUv2 | ADE20k |
+|-------|--------|---------|-----|-----------|------|-----|------|-------|-------|--------|
+| EUPE-ConvNeXt-T | 29M | 43.7 | 68.8 | 47.9 | 83.4 | 63.0 | 1278.1 | 41.3 | 0.430 | 43.5 |
+| EUPE-ConvNeXt-S | 50M | 45.0 | 68.9 | 50.5 | 84.0 | 64.7 | 1284.2 | 40.1 | 0.388 | 46.8 |
+| EUPE-ConvNeXt-B | 89M | 46.4 | 70.1 | 53.3 | 84.7 | 65.8 | 1348.9 | 37.7 | 0.365 | 48.9 |
+
+## Citation
+```bibtex
+@misc{zhu2026eupe,
+  title={Efficient Universal Perception Encoder},
+  author={Zhu, Chenchen and Suri, Saksham and Jose, Cijo and Oquab, Maxime and Szafraniec, Marc and Wen, Wei and Xiong, Yunyang and Labatut, Patrick and Bojanowski, Piotr and Krishnamoorthi, Raghuraman and Chandra, Vikas},
+  year={2026},
+  eprint={2603.22387},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2603.22387},
+}
+```
+```bibtex
+@article{liu2022convnet,
+  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+  title   = {A ConvNet for the 2020s},
+  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year    = {2022},
+}
+```
+```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}}
+}
+```

config.json

@@ -0,0 +1,36 @@
+{
+  "architecture": "convnext_small",
+  "num_classes": 0,
+  "num_features": 768,
+  "pretrained_cfg": {
+    "tag": "eupe_lvd1689m",
+    "custom_load": false,
+    "input_size": [
+      3,
+      256,
+      256
+    ],
+    "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": 0,
+    "pool_size": [
+      8,
+      8
+    ],
+    "first_conv": "stem.0",
+    "classifier": "head.fc",
+    "license": "fair-noncommercial-research-license"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92f00ac2f509f0b7b6f74b158c26be72421288dd46236374821325d8d71f29bc
+size 197852680