Add ViT-H v1.1 model
Browse files- README.md +15 -7
- models/tap_vit_h_v1_1.pkl +3 -0
README.md
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### Model weights
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| Model | Description | Schedule | MD5 | Weights |
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| ----- | ------------| ------ | ----| ------ |
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| **tap_vit_l** | ViT-L TAP v1.1 model | (100% SA-1B, 180k), (VG, 50ep) | c1d41f | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_l_v1_1.pkl) |
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| **tap_vit_b** | ViT-B TAP v1.1 model | (100% SA-1B, 180k), (VG, 50ep) | 707f80 | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_b_v1_1.pkl) |
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| **tap_vit_l** | ViT-L TAP v1.0 model | (50% SA-1B, 90k), (VG, 25ep) | 03f8ec | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_l_v1_0.pkl) |
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| **tap_vit_b** | ViT-B TAP v1.0 model | (50% SA-1B, 90k), (VG, 25ep) | b45cbf | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_b_v1_0.pkl) |
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#### V1.
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### Concept weights
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### Model weights
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#### V1.1 Release Notes
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- Three versions of the model are available with different image encoders.
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- Use a longer pre-training and fine-tuning schedule (improved segmentation and caption performance).
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- Apply weight decay for all bias parameters (avoid FP16 overflow in QK matmul).
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- Sample point prompts from predicted mask instead of GT box during VG training.
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| Model | Description | Schedule | MD5 | Weights |
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| ----- | ------------| ------ | ----| ------ |
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| **tap_vit_h** | ViT-H TAP v1.1 model | (100% SA-1B, 180k), (VG, 50ep) | 4bdfb9 | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_h_v1_1.pkl) |
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| **tap_vit_l** | ViT-L TAP v1.1 model | (100% SA-1B, 180k), (VG, 50ep) | c1d41f | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_l_v1_1.pkl) |
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| **tap_vit_b** | ViT-B TAP v1.1 model | (100% SA-1B, 180k), (VG, 50ep) | 707f80 | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_b_v1_1.pkl) |
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#### V1.0 Release Notes
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- Two versions of the model are available with different image encoders.
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- Original paper results.
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| Model | Description | Schedule | MD5 | Weights |
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| ----- | ------------| ------ | ----| ------ |
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| **tap_vit_l** | ViT-L TAP v1.0 model | (50% SA-1B, 90k), (VG, 25ep) | 03f8ec | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_l_v1_0.pkl) |
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| **tap_vit_b** | ViT-B TAP v1.0 model | (50% SA-1B, 90k), (VG, 25ep) | b45cbf | [π€ HF link](https://huggingface.co/BAAI/tokenize-anything/blob/main/models/tap_vit_b_v1_0.pkl) |
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### Concept weights
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models/tap_vit_h_v1_1.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d3caabfc826ad52f2c34f4fe6f575b70189d3effe837da9cf3264d080c40227
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size 1488947633
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