chaosbee997/rice-seed-classifier

籽粒分类模型（大米品种分类） / Grain Seed Classification Model

This repository contains the training script and (eventually) the fine-tuned model for classifying rice grain varieties from seed images.

Dataset

• Source: nateraw/rice-image-dataset
• Size: 75,000 RGB images (250×250)
• Classes (5):
  1. Arborio
  2. Basmati
  3. Ipsala
  4. Jasmine
  5. Karacadag
• License: CC0-1.0

Model

• Architecture: ResNet-18 (microsoft/resnet-18) — ~11M parameters, lightweight and fast
• Task: Multi-class image classification

How to train

Run the provided script on a GPU (e.g. a10g-large or t4-small via Hugging Face Jobs, or Google Colab):

pip install transformers datasets torch accelerate evaluate pillow trackio

export HF_MODEL_REPO=chaosbee997/rice-seed-classifier
export HF_TOKEN=your_huggingface_token

python train.py

Or submit via Hugging Face Jobs (requires GPU credits):

huggingface-cli job run \
  --script train.py \
  --hardware a10g-large \
  --timeout 4h \
  --dependencies "transformers,datasets,torch,accelerate,evaluate,pillow,trackio"

Expected results

• Typical fine-tuning on this dataset with ResNet-18 yields > 95% accuracy within 3-5 epochs.

Extending to other crops

The same script works for any datasets.ImageFolder-style dataset. To add peanut, corn, wheat, etc.:

1. Collect or find an image dataset with folder-per-class structure.
2. Upload it to Hugging Face Hub or point load_dataset to a local path.
3. Update MODEL_NAME if you want a different backbone (e.g. microsoft/resnet-34, google/mobilenet_v2_1.0_224).
4. Run train.py.

License

Apache-2.0

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "chaosbee997/rice-seed-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

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