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README.md

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 * YOLO-World presents a *prompt-then-detect* paradigm for efficient user-vocabulary inference, which re-parameterizes vocabulary embeddings as parameters into the model and achieve superior inference speed. You can try to export your own detection model without extra training or fine-tuning in our [online demo](https://huggingface.co/spaces/stevengrove/YOLO-World)!
 
 
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 <img width=800px src="./assets/yolo_arch.png">
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 ## Model Zoo
 
 We've pre-trained YOLO-World-S/M/L from scratch and evaluate on the `LVIS val-1.0` and `LVIS minival`. We provide the pre-trained model weights and training logs for applications/research or re-producing the results.
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-YOLO-World supports **zero-shot inference**, and three types of **fine-tuning recipes**: **(1) normal fine-tuning**, **(2) prompt tuning**, and **(3) reparameterized fine-tuning**.
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+<b><p>Chose your pre-trained YOLO-World and Fine-tune it!</p></b> 
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+YOLO-World supports **zero-shot inference**, and three types of **fine-tuning recipes**: **(1) normal fine-tuning**, **(2) prompt tuning**, and **(3) reparameterized fine-tuning**.
 
 * Normal Fine-tuning: we provide the details about fine-tuning YOLO-World in [docs/fine-tuning](./docs/finetuning.md).
 
 * Prompt Tuning: we provide more details ahout prompt tuning in [docs/prompt_yolo_world](./docs/prompt_yolo_world.md).
 
-* Reparameterized Fine-tuning: the reparameterized YOLO-World is more suitable for specific domains far from generic scenes. You can find more details in [`docs/reparameterize`](./docs/reparameterize.md).
+* Reparameterized Fine-tuning: the reparameterized YOLO-World is more suitable for specific domains far from generic scenes. You can find more details in [docs/reparameterize](./docs/reparameterize.md).
 
 ## Deployment
 
 We provide the details about deployment for downstream applications in [docs/deployment](./docs/deploy.md).
 You can directly download the ONNX model through the online [demo](https://huggingface.co/spaces/stevengrove/YOLO-World) in Huggingface Spaces 🤗.
 
+- [x] ONNX export and demo: [docs/deploy](https://github.com/AILab-CVC/YOLO-World/blob/master/docs/deploy.md)
+- [x] TFLite and INT8 Quantization: [docs/tflite_deploy](https://github.com/AILab-CVC/YOLO-World/blob/master/docs/tflite_deploy.md)
+- [ ] TensorRT: coming soon.
+- [ ] C++: coming soon.
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 ## Demo
 
 See [`demo`](./demo) for more details