Qat

.gitignore

@@ -129,7 +129,18 @@ dmypy.json
 .pyre/
 
 # Data
-Data
+*.jpg
+*.jpeg
+*.png
+*.xml
+*.txt
+*.json
+*.yaml
+*.mp4
 
 # Train & Inference
-runs
+*.pth
+*.onnx
+*.pkl
+*.0
+*.1

README.md

@@ -36,19 +36,31 @@ git clone https://github.com/naseemap47/YOLO-NAS.git
 cd YOLO-NAS
 ```
 ### Install dependencies
-**Recommended**:
+Create anaconda python environment
 ```
 conda create -n yolo-nas python=3.9 -y
 conda activate yolo-nas
-conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y
+```
+**PyTorch v1.11.0** Installation
+```
+# conda installation
+conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch -y
+
+/// OR
+
+# PIP installation
+pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
+```
+**Quantization Aware Training**
+```
 # For Quantization Aware Training
 pip install pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com
-pip install super-gradients==3.1.3
 ```
-#### OR
+Install **Super-Gradients**
 ```
-pip3 install -r requirements.txt
+pip install super-gradients==3.1.3
 ```
+
 ### 🎒 Prepare Dataset
 Your custom dataset should be in **COCO JSON** data format.<br>
 To convert **YOLO (.txt) / PASCAL VOC (.XML)** format to **COCO JSON**.<br>

qat.py

@@ -76,16 +76,16 @@
     print(f"\033[1m[INFO] Number of Classes: {no_class}\033[0m")
 
     # Training on GPU or CPU    
-    name, ckpt_dir = args['weight'].split('/')[-3:-1]
+    _, name = args['weight'].split('/')[-3:-1]
     if args['cpu']:
         print('[INFO] Training on \033[1mCPU\033[0m')
-        trainer = Trainer(experiment_name=name, ckpt_root_dir=ckpt_dir, device='cpu')
+        trainer = Trainer(experiment_name=name, ckpt_root_dir='qat', device='cpu')
     elif args['gpus']:
         print(f'[INFO] Training on GPU: \033[1m{torch.cuda.get_device_name()}\033[0m')
-        trainer = Trainer(experiment_name=name, ckpt_root_dir=ckpt_dir, multi_gpu=args['gpus'])
+        trainer = Trainer(experiment_name=name, ckpt_root_dir='qat', multi_gpu=args['gpus'])
     else:
         print(f'[INFO] Training on GPU: \033[1m{torch.cuda.get_device_name()}\033[0m')
-        trainer = Trainer(experiment_name=name, ckpt_root_dir=ckpt_dir)
+        trainer = Trainer(experiment_name=name, ckpt_root_dir='qat')
 
     # Load best model
     best_model = models.get(args['model'],

requirements.txt

@@ -1,2 +1 @@
-super-gradients==3.1.3
-# urllib3==1.25.9
+super-gradients==3.1.3