Update README.md (#2)

- Update README.md (9359633562e09e280ca0d1c4feb8f78a6bf4172d)


Co-authored-by: Karmesh Yadav <[email protected]>

README.md

@@ -12,15 +12,20 @@ tags:
 - embodied-ai
 - memory
 ---
+<center>
 <a href="https://arxiv.org/abs/2506.15635" target="_blank">
     <img alt="arXiv" src="https://img.shields.io/badge/arXiv-FindingDory-red?logo=arxiv" height="20" />
 </a>
 <a href="https://findingdory-benchmark.github.io/" target="_blank">
     <img alt="Website" src="https://img.shields.io/badge/🌎_Website-FindingDory-blue.svg" height="20" />
 </a>
-<a href="https://github.com/findingdory-benchmark/findingdory-trl" target="_blank" style="display: inline-block; margin-right: 10px;">
+<a href="https://github.com/findingdory-benchmark/findingdory-trl" target="_blank">
     <img alt="GitHub Code" src="https://img.shields.io/badge/Code-FindingDory--TRL-white?&logo=github&logoColor=white" />
 </a>
+<a href="https://huggingface.co/datasets/yali30/findingdory/" target="_blank"">
+    <img alt="Huggingface" src="https://img.shields.io/badge/Dataset-FindingDory-yellow?logo=huggingface" />
+</a>
+</center>
 
 <center><h1>FindingDory: A Benchmark to Evaluate Memory in Embodied Agents</h1>
   <a href="https://www.karmeshyadav.com/">Karmesh Yadav*</a>,
@@ -38,14 +43,14 @@ At deployment the image corresponding to the index is fed into a low-level navig
 🏋️ Training details
 | Property | Value |
 | -------- | ----- |
-| Epochs   | 5 |
+| Epochs   | 5 (Total training steps 12840) |
 | Effective batch | 32 |
 | LR schedule | Cosine (LR=5e-6, Warmup ratio=0.1)  |
-| Image resol. | TODO |
-| Compute  | “8 × A40 48 GB for ~18 hours” |
-| Input frames | 96 Images |
+| Max Pixels. | 360 x 420 |
+| Compute  | “8 × A40 48 GB for ~84 hours” |
+| Input frames | 96 Images (~10k tokens) |
 | Optimiser | AdamW(β₁ = 0.9, β₂ = 0.95) |
-| Best checkpoint | TODO |
+| Best checkpoint | 8800 Steps |
 
 
 📊 Evaluation
@@ -57,6 +62,7 @@ We compare the performance of our finetuned `FindingDory-Qwen2.5-VL-3B-SFT` chec
 | Gemma3-12B-it | 13.2% | zero-shot |
 | GPT-4o | 27.3% | zero-shot |
 | Gemini-2.0-Flash | 25.4% | zero-shot |
+
 Checkout Fig 2 in the paper for more details.
 
 📄 Citation