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bak/dino-siglip/vit_large_patch14_reg4_dinov2.lvd142m/.gitattributes

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bak/dino-siglip/vit_large_patch14_reg4_dinov2.lvd142m/README.md

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+---
+license: apache-2.0
+library_name: timm
+tags:
+- image-feature-extraction
+- timm
+---
+# Model card for vit_large_patch14_reg4_dinov2.lvd142m
+
+A Vision Transformer (ViT) image feature model with registers. Pretrained on LVD-142M with self-supervised DINOv2 method.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 304.4
+  - GMACs: 416.1
+  - Activations (M): 305.3
+  - Image size: 518 x 518
+- **Papers:**
+  - Vision Transformers Need Registers: https://arxiv.org/abs/2309.16588
+  - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193
+  - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
+- **Original:** https://github.com/facebookresearch/dinov2
+- **Pretrain Dataset:** LVD-142M
+
+## 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_large_patch14_reg4_dinov2.lvd142m', 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(
+    'vit_large_patch14_reg4_dinov2.lvd142m',
+    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, 1374, 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).
+
+## Citation
+```bibtex
+@article{darcet2023vision,
+  title={Vision Transformers Need Registers},
+  author={Darcet, Timoth{'e}e and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr},
+  journal={arXiv preprint arXiv:2309.16588},
+  year={2023}
+}
+```
+```bibtex
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+```
+```bibtex
+@article{dosovitskiy2020vit,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
+  journal={ICLR},
+  year={2021}
+}
+```
+```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}}
+}
+```

bak/dino-siglip/vit_large_patch14_reg4_dinov2.lvd142m/config.json

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+  "architecture": "vit_large_patch14_reg4_dinov2",
+  "num_classes": 0,
+  "num_features": 1024,
+  "global_pool": "token",
+  "pretrained_cfg": {
+    "tag": "lvd142m",
+    "custom_load": false,
+    "input_size": [
+      3,
+      518,
+      518
+    ],
+    "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",
+    "license": "apache-2.0"
+  }
+}

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bak/dino-siglip/vit_so400m_patch14_siglip_384/README.md

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+---
+tags:
+- clip
+- siglip
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+license: apache-2.0
+datasets:
+- webli
+---
+# Model card for ViT-SO400M-14-SigLIP-384
+
+A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
+
+This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
+
+## Model Details
+- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
+- **Original:** https://github.com/google-research/big_vision
+- **Dataset:** WebLI
+- **Papers:**
+  - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
+
+## Model Usage
+### With OpenCLIP
+```python
+import torch
+import torch.nn.functional as F
+from urllib.request import urlopen
+from PIL import Image
+from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
+
+model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
+tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
+
+image = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+image = preprocess(image).unsqueeze(0)
+
+labels_list = ["a dog", "a cat", "a donut", "a beignet"]
+text = tokenizer(labels_list, context_length=model.context_length)
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    image_features = F.normalize(image_features, dim=-1)
+    text_features = F.normalize(text_features, dim=-1)
+
+    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
+
+zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
+print("Label probabilities: ", zipped_list)
+```
+
+### With `timm` (for image embeddings)
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+image = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'vit_so400m_patch14_siglip_384',
+    pretrained=True,
+    num_classes=0,
+)
+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(image).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+```    
+
+## Citation
+```bibtex
+@article{zhai2023sigmoid,
+  title={Sigmoid loss for language image pre-training},
+  author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
+  journal={arXiv preprint arXiv:2303.15343},
+  year={2023}
+}
+```
+```bibtex
+@misc{big_vision,
+  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
+  title = {Big Vision},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/google-research/big_vision}}
+}
+```

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+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

checkpoints/pretrain-0618/checkpoint-500000/README.md

@@ -0,0 +1,10 @@
+---
+tags:
+- model_hub_mixin
+- pytorch_model_hub_mixin
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: https://huggingface.co/hongzhe2002/H-RDT
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

checkpoints/pretrain-0618/checkpoint-500000/config.json

@@ -0,0 +1,74 @@
+{
+  "act_adaptor": "mlp3x_silu",
+  "act_pos_emb_config": [
+    [
+      "state",
+      1
+    ],
+    [
+      "action",
+      16
+    ]
+  ],
+  "action_dim": 48,
+  "ema": {
+    "inv_gamma": 1.0,
+    "max_value": 0.9999,
+    "min_value": 0.0,
+    "power": 0.75,
+    "update_after_step": 0
+  },
+  "img_adapter": "mlp2x_silu",
+  "img_pos_emb_config": [
+    [
+      "image",
+      [
+        1,
+        1,
+        -729
+      ]
+    ]
+  ],
+  "lang_adapter": "mlp2x_silu",
+  "lang_pos_emb_config": [
+    [
+      "language",
+      -1024
+    ]
+  ],
+  "max_img_len": 729,
+  "max_lang_len": 1024,
+  "mode": "pretrain",
+  "noise_scheduler": {
+    "num_inference_timesteps": 5,
+    "sampler_type": "uniform",
+    "timestep_max": 0.999
+  },
+  "pred_horizon": 16,
+  "pretrained_backbone_path": null,
+  "hrdt": {
+    "depth": 16,
+    "ffn_dim_multiplier": null,
+    "hidden_size": 2176,
+    "multiple_of": 256,
+    "norm_eps": 1e-05,
+    "num_heads": 16,
+    "num_kv_heads": 8,
+    "output_size": 48,
+    "use_flash_attn": true
+  },
+  "st_adaptor": "mlp3x_silu",
+  "state_dim": 48,
+  "text": {
+    "feature_dim": 4096
+  },
+  "time_noise": {
+    "a": 5,
+    "beta_m": 100
+  },
+  "training_mode": "lang",
+  "video_adapter": "mlp2x_silu",
+  "vision": {
+    "feature_dim": 2176
+  }
+}

checkpoints/pretrain-0618/checkpoint-500000/pytorch_model.bin

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+oid sha256:e9eaff94ac1c1d039a193f04627b8c27794e826ca1eebd3de5eab60435254be2
+size 4137869178