Update model config and README

README.md

@@ -3,5 +3,127 @@ tags:
 - image-classification
 - timm
 library_tag: timm
+license: mit
+datasets:
+- imagenet-1k
+- imagenet-22k
 ---
-# Model card for eva_large_patch14_336.in22k_ft_in1k
+# Model card for eva_large_patch14_336.in22k_ft_in1k
+
+An EVA image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-1k by paper authors.
+
+NOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 304.5
+  - GMACs: 191.1
+  - Activations (M): 270.2
+  - Image size: 336 x 336
+- **Papers:**
+  - EVA: Exploring the Limits of Masked Visual Representation Learning at Scale: https://arxiv.org/abs/2211.07636
+- **Pretrain Dataset:** ImageNet-22k
+- **Dataset:** ImageNet-1k
+- **Original:**
+  - https://github.com/baaivision/EVA
+  - https://huggingface.co/BAAI/EVA
+
+## 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('eva_large_patch14_336.in22k_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)
+```
+
+### 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(
+    'eva_large_patch14_336.in22k_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, 577, 1024) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
+
+|model                                          |top1  |top5  |param_count|img_size|
+|-----------------------------------------------|------|------|-----------|--------|
+|eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08     |448     |
+|eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08     |448     |
+|eva_giant_patch14_560.m30m_ft_in22k_in1k       |89.792|98.992|1014.45    |560     |
+|eva02_large_patch14_448.mim_in22k_ft_in1k      |89.626|98.954|305.08     |448     |
+|eva02_large_patch14_448.mim_m38m_ft_in1k       |89.57 |98.918|305.08     |448     |
+|eva_giant_patch14_336.m30m_ft_in22k_in1k       |89.56 |98.956|1013.01    |336     |
+|eva_giant_patch14_336.clip_ft_in1k             |89.466|98.82 |1013.01    |336     |
+|eva_large_patch14_336.in22k_ft_in22k_in1k      |89.214|98.854|304.53     |336     |
+|eva_giant_patch14_224.clip_ft_in1k             |88.882|98.678|1012.56    |224     |
+|eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12      |448     |
+|eva_large_patch14_336.in22k_ft_in1k            |88.652|98.722|304.53     |336     |
+|eva_large_patch14_196.in22k_ft_in22k_in1k      |88.592|98.656|304.14     |196     |
+|eva02_base_patch14_448.mim_in22k_ft_in1k       |88.23 |98.564|87.12      |448     |
+|eva_large_patch14_196.in22k_ft_in1k            |87.934|98.504|304.14     |196     |
+|eva02_small_patch14_336.mim_in22k_ft_in1k      |85.74 |97.614|22.13      |336     |
+|eva02_tiny_patch14_336.mim_in22k_ft_in1k       |80.658|95.524|5.76       |336     |
+
+## Citation
+```bibtex
+@article{EVA,
+  title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
+  author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
+  journal={arXiv preprint arXiv:2211.07636},
+  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

@@ -28,6 +28,7 @@
     "num_classes": 1000,
     "pool_size": null,
     "first_conv": "patch_embed.proj",
-    "classifier": "head"
+    "classifier": "head",
+    "license": "mit"
   }
 }

model.safetensors

@@ -0,0 +1,3 @@
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+size 1218153886