conveyor-belt-recycling-detector

SAM3 + Qwen3-VL Conveyor Belt Recycling Detector

64-class recyclable material detection & counting on conveyor belts, deployed on Jetson AGX Orin 64GB

Architecture

Two-stage pipeline:

1. SAM3 (facebook/sam3) — segments all objects via text-prompted concept segmentation
2. Qwen3-VL (Qwen/Qwen3-VL-2B-Instruct) — classifies each crop into 64 recycling categories

64 Material Classes

Deployment on Jetson AGX Orin 64GB

Quick Start

git clone https://huggingface.co/Adit1122/conveyor-belt-recycling-detector
cd conveyor-belt-recycling-detector
chmod +x setup_jetson.sh
sudo ./setup_jetson.sh

Performance

REST API (port 8080)

curl http://localhost:8080/counts    # Get item counts
curl http://localhost:8080/latest    # Latest detections
curl -X POST http://localhost:8080/detect --data-binary @frame.jpg  # Analyze image

Files

• conveyor_belt_detector.py — Core detection pipeline (works on any GPU)
• jetson_inference_server.py — Jetson-optimized server with camera + REST API
• setup_jetson.sh — Automated setup for JetPack 6.2+
• Dockerfile.jetson — Container deployment option
• config.json — Configuration file
• conveyor_belt_detector.service — systemd service unit

Requirements

• Jetson AGX Orin 64GB with JetPack 6.2+
• USB/CSI/IP camera (1080p overhead view)
• HuggingFace account with SAM3 access (request here)

Models Used

• facebook/sam3 — 860M params, gated
• Qwen/Qwen3-VL-2B-Instruct — open, Apache 2.0

---

library_name: transformers tags:

• object-detection
• recycling
• conveyor-belt
• sam3
• qwen3-vl
• jetson
• edge-ai

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Adit1122/conveyor-belt-recycling-detector"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.