README.md

---
tags:
- ml-intern
---
# 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`](https://huggingface.co/datasets/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):

```bash
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):

```bash
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

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/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

```python
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.