Update README.md

updated model info and path directory material. Github has most info you need.

README.md

@@ -1,11 +1,11 @@
 ---
+license: apache-2.0
 tags:
 - computer-vision
 - robotics
 - articulated-objects
 language:
 - en
-license: mit
 ---
 
 # L-CAMP: Language-Conditioned Axis and Motion Prediction for Articulated Object Manipulation
@@ -24,28 +24,45 @@ To use it, download `lcamp_model.pt` from the Files tab and place it in a `check
 
 ## Setup
 
-1. Clone the [GitHub repo](https://github.com/UTNuclearRobotics/l_camp) and create the conda/micromamba environment from `environment_lcamp.yml`:
+1. Clone the [GitHub repo](https://github.com/UTNuclearRobotics/l_camp) and create the conda environment from `environment_lcamp.yml`:
    ```bash
-   micromamba create -f environment_lcamp.yml   # or: conda env create -f environment_lcamp.yml
-   micromamba activate lcamp
+   conda create -f environment_lcamp.yml 
+   conda activate lcamp
    ```
 2. Build the LCAMP dataset (only needed once per dataset version), see
-   `process_lcamp_dataset.py` / `process_lcamp_dataset.slurm`. This produces a
-   dataset directory containing `train_dataset.json`, `test_dataset.json`,
-   and `intrinsics.npy`, referenced below as `--data_root`/`--data_dir`.
+   `process_lcamp_dataset.py`. Whatever path you pass as its `--data_dir` is
+   the dataset directory (containing `train_dataset.json`, `test_dataset.json`,
+   and `intrinsics.npy`) referenced below as `<DATASET_DIR>`
+   (`--data_root`/`--data_dir` in `train.py`/`eval.py`).
+3. Get the background images: `LCAMPDataset` (in `lcamp_dataset.py`)
+   unconditionally loads a background image list on init (used for the
+   `--randomize_background`/`--random_backgrounds` augmentation, but read
+   regardless), sourced from the
+   [MIT Indoor 67](http://web.mit.edu/torralba/www/indoor.html) scene dataset.
+   From that page, download the images archive and the `TrainImages.txt` /
+   `TestImages.txt` split files, then lay them out as:
+   ```
+   backgrounds/mit_indoor_67/TrainImages.txt
+   backgrounds/mit_indoor_67/TestImages.txt
+   backgrounds/mit_indoor_67/raw/Images/<category>/<image>.jpg
+   ```
+   relative to wherever you run `train.py`/`eval.py` from (or under
+   `$SCRATCH/backgrounds/mit_indoor_67/...` if using `--cluster_loc
+   stampede3` — see the `--cluster_loc` note below).
 
 All commands below (`train.py`, `eval.py`) assume the `lcamp` environment is
 active.
 
 ## Training
 
-If you'd rather train your own checkpoint instead of using this one, training is done with `train.py` via `torchrun` (DDP). The dataset must already be built (see `process_lcamp_dataset.py` / `process_lcamp_dataset.slurm`) as a directory containing `train_dataset.json`, `test_dataset.json`, and `intrinsics.npy`.
+If you'd rather train your own checkpoint instead of using this one, training is done with `train.py` via `torchrun` (DDP). The dataset must already be built (see `process_lcamp_dataset.py`) as a directory containing `train_dataset.json`, `test_dataset.json`, and `intrinsics.npy`.
 
-Single-GPU / local run:
+Set `--nproc_per_node` to the number of GPUs on your machine (use `1` for a
+single-GPU/local run):
 ```bash
-torchrun --nproc_per_node=1 train.py \
+torchrun --nproc_per_node=<NUM_GPUS> train.py \
     --save_path checkpoints/my_lcamp_model \
-    --data_root ./lcamp_dataset_limited_dataset \
+    --data_root <DATASET_DIR> \
     --model_type resnet \
     --epochs 50 \
     --batch_size 64 \
@@ -55,36 +72,13 @@ torchrun --nproc_per_node=1 train.py \
     --use_depth \
     --use_text_instructions \
     --emphasize_part_mask \
+    --bbox_loc \
+    --cluster_loc <CLUSTER_LOC> \
     --lambda_axis 8 \
-    --lambda_anchor 2 \
+    --lambda_anchor 8 \
     --lambda_joint 1
 ```
 
-Multi-GPU (cluster) example:
-```bash
-torchrun --nproc_per_node=8 train.py \
-    --save_path "$SCRATCH/lcamp_model_RGB_log_norm_DEPTH_NO_AXIS_SYMMETRY" \
-    --data_root "$SCRATCH/lcamp_dataset_off_target_depth_LANGUAGE_EXPLICIT_OBJ_CMDS" \
-    --model_type resnet \
-    --epochs 50 \
-    --warmup_epochs 5 \
-    --batch_size 256 \
-    --lr 1e-3 \
-    --min_lr 1e-5 \
-    --use_camera_frame \
-    --use_depth \
-    --use_text_instructions \
-    --emphasize_part_mask \
-    --randomize_background \
-    --lambda_axis 8 \
-    --lambda_anchor 4 \
-    --lambda_joint 1 \
-    --lambda_plucker 0 \
-    --log_dir "$SCRATCH/train_logs" \
-    --cluster_loc "stampede3"
-```
-See `train.slurm` / `train_h100.slurm` in the GitHub repo for full Slurm submission scripts (note: those scripts currently hold older `--lr`/`--lambda_anchor`/`--lambda_joint` values, update them if you want the Slurm jobs to match the above).
-
 Notable flags (`train.py --help` for the complete list):
 - `--model_type`: `resnet` (default LCAMP backbone) or `dino`.
 - `--use_text_instructions`: enables the language-conditioned model
@@ -94,10 +88,20 @@ Notable flags (`train.py --help` for the complete list):
 - `--bbox_loc`: predict axis location as `(u,v,z)` decoded via the part
   bounding box instead of raw XYZ, must match `--bbox_loc` in `eval.py` and
   `use_bbox_loc` in `lcamp_service_node.py`/`lcamp.launch.py` for the same
-  checkpoint.
+  checkpoint. Whenever this is active, pass `--bbox_loc` explicitly so the
+  flag stays consistent across `train.py`/`eval.py`/deployment.
 - `--resume` / `--resume_from <path>`: resume from `checkpoint_best.pt` in
   `--save_path`, or from an explicit checkpoint path.
 - `--save_freq`, `--eval_freq`: checkpoint/eval cadence in epochs.
+- `--cluster_loc`: the dataset JSON (from `process_lcamp_dataset.py`) stores
+  absolute file paths for the rgb/depth/mask files, so if you're loading the
+  dataset on a different machine/path layout than where it was built, those
+  paths won't resolve. `nrg` (default) uses the JSON paths as-is, `stampede3`
+  is a legacy alias for `$SCRATCH`. For anything else, just pass your own
+  dataset root directory as `--cluster_loc /path/to/your/root` — `lcamp_
+  dataset.py` will swap it in for the legacy `/storage/danieladebi` prefix in
+  the rgb/depth/mask paths, and root the background images (see above) under
+  it too. No code changes needed.
 
 Checkpoints are written to `--save_path` as `checkpoint_best.pt`,
 `checkpoint_last.pt`, and periodic `checkpoint_{epoch}.pt` files.
@@ -108,12 +112,14 @@ Evaluation is done with `eval.py` (single process, no DDP). To evaluate this che
 ```bash
 python eval.py \
     --model_path checkpoints/lcamp_model.pt \
-    --data_dir ./lcamp_dataset_limited_dataset \
+    --data_dir <DATASET_DIR> \
     --split test \
     --model_type resnet \
     --use_camera_frame \
     --use_depth \
     --use_text_instructions \
+    --bbox_loc \
+    --cluster_loc <CLUSTER_LOC> \
     --visualize
 ```
 
@@ -144,4 +150,4 @@ launch file for details on `use_bbox_loc` and other runtime flags).
 
 Running predictions on a real Boston Dynamics Spot (via the ROS2 service node
 in `scripts/lcamp_service_node.py`) is outside the scope of this model card,
-see [ros2_setup/ROS2_SETUP.md](https://github.com/UTNuclearRobotics/l_camp/blob/main/ros2_setup/ROS2_SETUP.md) in the GitHub repo for that setup.
+see [ros2_setup/ROS2_SETUP.md](https://github.com/UTNuclearRobotics/l_camp/blob/main/ros2_setup/ROS2_SETUP.md) in the GitHub repo for that setup.