Unfolding Robotics: Open-Source Shirt Folding from Data to Deployment
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Explore the open-source guide to robot shirt folding
Koen Botermans
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lerobot/robot-folding reacted to dronefreak's post with 🧠 5 days ago
Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome. reacted to dronefreak's post with ❤️ 5 days ago
Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome.Organizations
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Space about 14 hours ago
reacted to dronefreak's post with 🧠❤️🚀🔥 5 days ago
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Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome.
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reacted to mitkox's post with 🚀🔥 7 months ago
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I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. It’s a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config.
Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.
Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,
Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.
Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,
- 5 replies
upvoted an article 7 months ago
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Introducing MTEB v2: Evaluation of embedding and retrieval systems for more than just text
isaacchung
• • 38 replied to sherryxychen's post 7 months ago
Hi @sherryxychen ,
Thanks a lot for the writing this down, what you wrote down really helped!
But I have a question about "Built proper eval pipeline with progress scoring" What do you mean exactly with this? I assume it is from this file: https://github.com/sherrychen1120/so101_bench/blob/main/src/so101_bench/task_progress_labeler.py
But I can't really figure out what happens in that file.
Thanks again!
reacted to sherryxychen's post with 🤗 7 months ago
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**Training ACT on SO-101: From Woodpecker to 90% Success (All the Mistakes Included)**
I spent 3 weeks training Action Chunking Transformer on SO-101 for pick-and-place. Spoiler: the first attempt trained a woodpecker that just pecked the table. 🐦
**What's Different About This Post:**
Most ACT tutorials show the success. I documented every failure, hardware issue, and debugging step. If you're new to SO-101/LeRobot/ACT, hopefully my mistakes save you time.
Try 1: The Woodpecker
- Followed the LeRobot tutorial, collected 50 episodes
- Beautiful loss curves ✅
- Robot learned to peck at table ❌
- Rookie mistakes: moving cameras, arm calibration mismatch, limited data diversity, looking at follower arm during teleop (it's cheating!)
Try 2: Engineering Upgrades
- Fixed hardware setup (tape + markers everywhere)
- USB udev rules for camera stability
- Formal task definition with stratified sampling
- Built proper eval pipeline with progress scoring
- Motor breakdown mid-collection (broke the gripper with excessive force 💀)
- Results: 60% in-distribution success, 10% OOD (better, but not great)
Try 3: More & Better Data
sherryxychen/2025-09-07_act
- 125 episodes with rotation variations ( sherryxychen/2025-09-01_pick-and-place-block)
- Better workspace coverage
- Improved grasping technique
- Results: 90% in-distribution success, 75% OOD! 🎉
Key Learnings:
- Consistent hardware setup is everything
- Don't look at the follower arm during teleop
- Data diversity is key for generalization
- Debug infrastructure matters
- Real robots break in mysterious ways (buy spare motors!)
Full write-up (with more videos!): https://huggingface.co/blog/sherryxychen/train-act-on-so-101
Code: https://github.com/sherrychen1120/so101_bench
Happy to answer any questions! 🤗
#imitation-learning #lerobot #act #so-101
I spent 3 weeks training Action Chunking Transformer on SO-101 for pick-and-place. Spoiler: the first attempt trained a woodpecker that just pecked the table. 🐦
**What's Different About This Post:**
Most ACT tutorials show the success. I documented every failure, hardware issue, and debugging step. If you're new to SO-101/LeRobot/ACT, hopefully my mistakes save you time.
Try 1: The Woodpecker
- Followed the LeRobot tutorial, collected 50 episodes
- Beautiful loss curves ✅
- Robot learned to peck at table ❌
- Rookie mistakes: moving cameras, arm calibration mismatch, limited data diversity, looking at follower arm during teleop (it's cheating!)
Try 2: Engineering Upgrades
- Fixed hardware setup (tape + markers everywhere)
- USB udev rules for camera stability
- Formal task definition with stratified sampling
- Built proper eval pipeline with progress scoring
- Motor breakdown mid-collection (broke the gripper with excessive force 💀)
- Results: 60% in-distribution success, 10% OOD (better, but not great)
Try 3: More & Better Data
sherryxychen/2025-09-07_act
- 125 episodes with rotation variations ( sherryxychen/2025-09-01_pick-and-place-block)
- Better workspace coverage
- Improved grasping technique
- Results: 90% in-distribution success, 75% OOD! 🎉
Key Learnings:
- Consistent hardware setup is everything
- Don't look at the follower arm during teleop
- Data diversity is key for generalization
- Debug infrastructure matters
- Real robots break in mysterious ways (buy spare motors!)
Full write-up (with more videos!): https://huggingface.co/blog/sherryxychen/train-act-on-so-101
Code: https://github.com/sherrychen1120/so101_bench
Happy to answer any questions! 🤗
#imitation-learning #lerobot #act #so-101
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