File size: 2,305 Bytes
c165272 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # Install Environment (Conda) and Run Scripts
This guide sets up a Conda environment and runs both scripts:
- `assignment_image/code/c1.py` (train + save checkpoint)
- `assignment_image/code/c1_test.py` (evaluate + error analysis)
First need to enter this folder:
```bash
cd assignment_llm_1/assignment_image
```
## 1) Create and activate Conda environment
```bash
conda create -n transformer_hw python=3.10 -y
conda activate transformer_hw
python -m pip install --upgrade pip
```
## 2) Install dependencies
If there is a `requirements.txt` file in this folder, run:
```bash
pip install -r requirements.txt
```
## 3) Run training script (`c1.py`)
Move to the code directory and run:
```bash
python code/c1.py
```
Expected outputs include:
- `saved_model/vit_cifar10_best.pt`
- `saved_model/vit_cifar10_last.pt`
## 4) Run evaluation script (`c1_test.py`)
After training completes:
```bash
python code/c1_test.py \
--checkpoint-path ./saved_model/vit_cifar10_best.pt \
--results-dir ./results
```
This baseline evaluation run saves:
- `results/baseline_analysis.txt`
- `results/misclassified_examples_test.png`
## 5) Run optional pre-trained ViT comparison
To run transfer learning and compare baseline vs pre-trained ViT:
```bash
python code/c1_test.py \
--checkpoint-path ./saved_model/vit_cifar10_best.pt \
--results-dir ./results \
--run-pretrained-experiment
```
Additional files saved in this mode:
- `results/pretrained_vit_analysis.txt`
- `results/misclassified_examples_pretrained_vit.png`
- `results/comparison_report.txt`
## 6) Where data and outputs are saved
- **Dataset download/cache**: `./data`
(both `c1.py` and `c1_test.py` load CIFAR-10 from this folder by default)
- **Model checkpoints from training**: `./saved_model`
- **Evaluation artifacts/reports**: `./results` (or the path passed with `--results-dir`)
- **Default checkpoint used by evaluation**: `./saved_model/vit_cifar10_best.pt`
### Quick path summary
- Training command: `python code/c1.py`
- Baseline evaluation: `python code/c1_test.py --checkpoint-path ./saved_model/vit_cifar10_best.pt --results-dir ./results`
- Baseline + transfer comparison: `python code/c1_test.py --checkpoint-path ./saved_model/vit_cifar10_best.pt --results-dir ./results --run-pretrained-experiment`
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