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Volkopat/
DeepSeek-DeepEncoder

Name: DeepSeek-DeepEncoder
Rating: 2.5 (15 reviews)

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DeepEncoder (Extracted from DeepSeek-OCR)

Overview

This directory contains the encoder components extracted from DeepSeek-OCR.

Model Files

sam_encoder.pth: SAM ViT-B encoder (95,569,152 params, 364.6 MB)
clip_encoder.pth: CLIP-Large encoder (303,177,728 params, 1156.6 MB)
projector.pth: Linear projector (2,622,720 params, 10.0 MB)
config.json: Model configuration

Total: 401,369,600 parameters

Architecture

Image (1024×1024) → SAM (95M) → 16× Conv → CLIP (303M) → Projector (3M) → 256 vision tokens

Usage

python

import torch
from deepencoder import build_sam_vit_b, build_clip_l, MlpProjector
from easydict import EasyDict as adict

# Load models
sam = build_sam_vit_b(checkpoint=None)
sam.load_state_dict(torch.load('sam_encoder.pth'))

clip = build_clip_l()
clip.load_state_dict(torch.load('clip_encoder.pth'))

projector_cfg = adict({'projector_type': 'linear', 'input_dim': 2048, 'n_embed': 1280})
projector = MlpProjector(projector_cfg)
projector.load_state_dict(torch.load('projector.pth'))

# Run encoder
vision_tokens = encode(image)  # [1, 256, 1280]

Training

These weights are:

Initialized from pretrained SAM (SA-1B) + CLIP (LAION-2B)
Fine-tuned together on optical compression/OCR tasks
Optimized for text preservation in compressed form

Source

Extracted from: deepseek-ai/DeepSeek-OCR

Volkopat/DeepSeek-DeepEncoder