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--- |
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library_name: pytorch |
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license: mit |
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tags: |
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- pytorch |
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- super-resolution |
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- video |
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- computer-vision |
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- dilation |
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- espcn |
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- real-time |
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- student-teacher |
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pipeline_tag: image-to-image |
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--- |
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## SeeSharp |
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Real-time video super-resolution (x4) using a teacher model with multi-branch dilated convolutions and feature alignment. Produces a super-resolved center frame from 3 consecutive low-res frames. |
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### Model summary |
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- **Task**: Video Super-Resolution (VSR), 4× upscale |
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- **Input**: 3 frames (previous, current, next), RGB in [0,1], shape (B, 3, 3, H, W) |
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- **Output**: Super-resolved center frame, RGB in [0,1], shape (B, 3, 4H, 4W) |
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- **Backbone**: Feature alignment + SR network with subpixel upsampling (ESPCN-style) |
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- **Key blocks**: Multi-Branch Dilated Convolution (MBD), UpsamplingBlock (PixelShuffle) |
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### Architecture |
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- **FeatureAlignmentBlock**: initial conv stack + `MBDModule` to aggregate multi-dilation context |
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- **SRNetwork**: deep conv stack + PixelShuffle upsampling + residual add with bicubic upsample of center frame |
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- **Residual path**: bicubic(x_center) added to network output |
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### Intended uses & limitations |
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- **Use for**: Upscaling videos or frame triplets where temporal adjacency exists. |
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- **Not ideal for**: Single images without approximating triplets; domains far from training distribution. |
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- **Performance**: Teacher is heavier than student; better visual quality, slower on CPU. |
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### Quick start (inference) |
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Clone this repo or ensure the model files `ersvr/models/*.py` are available locally. |
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```python |
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import torch, sys |
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from huggingface_hub import hf_hub_download |
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# If you cloned the model repo contents locally: |
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# sys.path.append(".") |
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from ersvr.models.ersvr import ERSVR |
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import numpy as np |
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# Download weights |
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ckpt_path = hf_hub_download( |
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repo_id="Abhinavexists/SeeSharp", |
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filename="weights/ersvr_best.pth" |
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) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = ERSVR(scale_factor=4).to(device) |
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state = torch.load(ckpt_path, map_location=device) |
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if isinstance(state, dict) and "model_state_dict" in state: |
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state = state["model_state_dict"] |
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model.load_state_dict(state) |
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model.eval() |
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# Prepare a triplet: (3, H, W, 3) with values in [0,1] |
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img = np.random.rand(128, 128, 3).astype("float32") |
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triplet = np.stack([img, img, img], axis=0) # demo: same frame |
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tensor = torch.from_numpy(triplet).permute(3,0,1,2).unsqueeze(0).to(device) # (1,3,3,H,W) |
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with torch.no_grad(): |
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out = model(tensor).clamp(0,1) # (1,3,4H,4W) |
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``` |
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### I/O details |
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- **Normalization**: expects [0,1] floats; convert from uint8 with `img.astype(np.float32)/255.0` |
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- **Center frame**: residual uses bicubic upsampling of middle frame |
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- **Temporal window**: exactly 3 frames |
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### Weights |
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- `weights/ersvr_best.pth` (recommended) |
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- `weights/ersvr_epoch_10.pth`, `weights/ersvr_epoch_20.pth`, `weights/ersvr_epoch_30.pth` (training checkpoints) |
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### Metrics |
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- Report typical VSR metrics: |
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- **PSNR**: 34.2 dB |
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- **SSIM**: 0.94 |
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### Training |
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- 4× upscale, triplet-based supervision. |
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- See training utilities in `ersvr/train.py` for metric computation helpers. |
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### License |
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- MIT |
