Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- laion-en
+- laion-zh
+- coyo
+- grit
+- coco
+- textcaps
+- objects365
+- openimages
+- all-seeing
+- wukong-ocr
+- laioncoco-ocr
+- other-ocr
+---
+# Model card for vit_intern300m_patch14_448.ogvl_dist
+
+An InternViT image feature model. Pretrained with distillation from [InternViT-6B](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) by paper authors with a wide variety of image-text data. Model weights have been converted from original to `timm` vit from [OpenGVLab/InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px). NOTE: this vit has no final norm before features / head.
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 304.0
+  - GMACs: 362.0
+  - Activations (M): 656.4
+  - Image size: 448 x 448
+- **Papers:**
+  - InternVL2: Better than the Best: https://internvl.github.io/blog/2024-07-02-InternVL-2.0/
+  - InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks: https://arxiv.org/abs/2312.14238
+- **Original:** https://github.com/OpenGVLab/InternVL
+- **Dataset:**
+  - LAION-en
+  - LAION-zh
+  - COYO
+  - GRIT
+  - COCO
+  - TextCaps
+  - Objects365
+  - OpenImages
+  - All-Seeing
+  - Wukong-OCR
+  - LaionCOCO-OCR
+  - other-OCR
+
+## 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('vit_intern300m_patch14_448.ogvl_dist', 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(
+    'vit_intern300m_patch14_448.ogvl_dist',
+    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, 1024, 32, 32])
+    #  torch.Size([1, 1024, 32, 32])
+    #  torch.Size([1, 1024, 32, 32])
+
+    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(
+    'vit_intern300m_patch14_448.ogvl_dist',
+    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, 1025, 1024) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Citation
+```bibtex
+@article{chen2023internvl,
+  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
+  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+  journal={arXiv preprint arXiv:2312.14238},
+  year={2023}
+}
+```
+```bibtex
+@article{chen2023internvl,
+  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
+  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+  journal={arXiv preprint arXiv:2312.14238},
+  year={2023}
+}
+```

config.json

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+{
+  "architecture": "vit_intern300m_patch14_448",
+  "num_classes": 0,
+  "num_features": 1024,
+  "global_pool": "token",
+  "pretrained_cfg": {
+    "tag": "ogvl_dist",
+    "custom_load": false,
+    "input_size": [
+      3,
+      448,
+      448
+    ],
+    "fixed_input_size": true,
+    "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": null,
+    "first_conv": "patch_embed.proj",
+    "classifier": "head"
+  }
+}

model.safetensors

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pytorch_model.bin

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