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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.
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